Coalesce Online

Welcome remarks

Coalesce New Orleans

Breakfast

Ecosystem talk w/ Snowplow Analytics

Ecosystem Sponsor

The off-Broadway, on-Bourbon adaption of "Data—The Musical." Featuring an all star cast you probably don't know outside that one humerous Twitter exchange, caroling a cathartic chorus of communal data dolors. The internal tracking tag our events team added to this session was, "Rad experiential social content" and I, the writer of this session summary, thought that said, "Rad experimental social construct" and I for one would like to see which ends up being true. Join me.

The off-Broadway, on-Bourbon adaption of &quot;Data—The Musical.&quot; Featuring an all star cast you probably don't know outside that one humerous Twitter exchange, caroling a cathartic chorus of communal data dolors. The internal tracking tag our events team added to this session was, &quot;Rad experiential social content&quot; and I, the writer of this session summary, thought that said, &quot;Rad experimental social construct&quot; and I for one would like to see which ends up being true. Join me.

Tiankai loves to combine his analytical mindset with creative thinking, and has been using his musical talents to make data more fun in all his career so far.

He worked for almost 10 years in Digital Marketing Analytics and is now leading the Product Data Governance team at adidas. A focus on people is important to him, and he is an ambassador for a more empathetic collaboration when it comes to data.

Senior Director, Product Data Governance

Data - The Musical

Emerging Voices

Community Vibes

Ecosystem talk w/ Spectacles 

As analytics engineers, we make impact by building analytics things (models, pipelines, visualizations) that help stakeholders make decisions about what to do next. What if we could also make impact by driving a culture of experimentation—which will help those same stakeholders make decisions too? 

Join Adam Stone (Netlify) as he draws on his vast experimentation experience and explains how analytics engineer can use a combination of a program-building mindset, organizational mentoring (and cheerleading), and off-the-shelf tools to partner with product and engineering teams to quickly spin up meaningful experimentation. 

As analytics engineers, we make impact by building analytics things (models, pipelines, visualizations) that help stakeholders make decisions about what to do next. What if we could also make impact by driving a culture of experimentation—which will help those same stakeholders make decisions too?&nbsp;Join Adam Stone (Netlify) as he draws on his vast experimentation experience and explains how analytics engineer can use a combination of a program-building mindset, organizational mentoring (and cheerleading), and off-the-shelf tools to partner with product and engineering teams to quickly spin up meaningful experimentation.

I've been an analytics engineer & data scientist for more than five years now, for tech companies small and big. Before this, I was a kindergarten teacher and a cognitive neuroscientist (not at the same time, though). Originally from San Diego, I now roost in London as a Senior Analytics Engineer at Netlify.

Analytics Engineer

What does analytics engineering have to do with product experimentation?

Mindset Shift

Blurred Boundaries - Engineering

Leading Voices

In this talk, Marielle Dado (Paddle) will introduce the concept of knowledge construction (a theory that featured heavily in their previous life as a learning psychologist/cognitive scientist), why they think analytics engineers should see themselves as "knowledge creators," and how this framing can lead to less ad-hoc querying and more robust data models.



In this talk, Marielle Dado (Paddle) will introduce the concept of knowledge construction (a theory that featured heavily in their previous life as a learning psychologist/cognitive scientist), why they think analytics engineers should see themselves as "knowledge creators," and how this framing can lead to less ad-hoc querying and more robust data models.

I'm a data engineer based in Berlin, Germany. In a past life, I was a learning sciences researcher (PhD), which means I combined theories from psychology and methods from computer science to understand how people learn.

I also really love cats, good food and specialty coffee.

Data Engineer

I don’t build models, I construct knowledge

Navigate Your Career

Ecosystem talk

A Senior Business Analyst, an Analytics Engineer, and a Data Quality Engineer walk into the office. All three of them ask for dbt. Why? Join Coco Haury (WEX, Inc.) to hear about the universal appeal of dbt to folks outside traditional data engineer and analyst roles, and how dbt has helped WEX modernize their entire enterprise data platform.

Coco is a high key nerd with jock tendencies who drinks too much coffee and loves animals. When she's not trying to change the world through the power of positive team collaboration, high quality testing, and efficient development processes, you can find Coco roller skating on the Maine coast.

Senior Data Quality Engineer

Finding common ground, to build common ground: dbt at the enterprise

Blurred Boundaries - Operations

Leading Teams

Strategies for Scale

This fast paced course will focus on rapidly learning some of the basics of dbt. We will rapidly explain concepts and copy paste code snippets with you to experience the power of dbt.  You will walk away understanding how dbt enables rapid development of transformation code, quality assurance, and deploying your transformations on a schedule. More details coming soon!

This fast paced course will focus on rapidly learning some of the basics of dbt. We will rapidly explain concepts and copy paste code snippets with you to experience the power of dbt. &nbsp;You will walk away understanding how dbt enables rapid development of transformation code, quality assurance, and deploying your transformations on a schedule. More details coming soon!

Course 1: Zero to dbt

Training

Do you enjoy waking up in the morning and playing the daily Wordle puzzle? Have you been wishing there was a similar game for you to play, but was built specifically for data folks? Well, you are in luck! 

Join Joe Markiewicz (analytics engineer by day, video game maker by night) as he explains his inspiration and how he leveraged dbt and BigQuery to create a new video game aimed at helping experienced analysts keep their SQL skills sharp and data newcomers increase their SQL literacy.

Do you enjoy waking up in the morning and playing the daily Wordle puzzle? Have you been wishing there was a similar game for you to play, but was built specifically for data folks? Well, you are in luck!&nbsp;Join Joe Markiewicz (analytics engineer by day, video game maker by night) as he explains his inspiration and how he leveraged dbt and BigQuery to create a new video game aimed at helping experienced analysts keep their SQL skills sharp and data newcomers increase their SQL literacy.

Video Game Developer

SQL: The video game

Technical Impact

Lunch

Move Fast and (Don’t) Break Things: Testing dbt Models with Bigeye

The tech talent shortage is now just the tech talent situation—it’s here to stay. But when growth is glamorous, throwing bodies at a problem is still winning out over doing more with less. Until this year. The current economic climate is showing us that it’s time to make “efficient” the new “sexy.” In this talk, Matthieu DiMercurio (Dialogue.co) shares how to cultivate executive buy-in to unite Data, Engineering, and IT in a joint endeavor to automate tasks, build smarter systems, and cut costly silos.

Matthieu is VP, Tech Operations at Dialogue Health Technologies, the Canadian leader in virtual care.
In his role, he leads the data platform, analytics, IT, Security and support teams.
Before joining Dialogue, Matthieu worked as a data scientist/engineer/analyst/[insert data-anything role here] at Intel Security and Ubisoft.
Matthieu is the cofounder and organizer of MTL DATA, the largest data community in Montreal.
He is also a subject matter expert at MIT Horizon, supporting the development of a training program in Big Data.

VP, Tech-ops

Efficient is the new sexy — A minimalist approach to growth

Beyond pretty graphs: How end-to-end lineage drives better actions

M&A can be fast-paced and messy, but companies often count on the data team to provide stability in metrics and data consolidation. After Dutchie acquired two companies in March 2021, the data team needed to integrate datasets from those companies into existing data platforms, and has some takeaways that may be helpful towards data practitioners one day finding themselves in a similar situation.

M&amp;A can be fast-paced and messy, but companies often count on the data team to provide stability in metrics and data consolidation. After Dutchie acquired two companies in March 2021, the data team needed to integrate datasets from those companies into existing data platforms, and has some takeaways that may be helpful towards data practitioners one day finding themselves in a similar situation.

Jacob Adler is the Analytics Engineering Lead at Dutchie, where he was the first data hire and built out the data function using dbt, Snowflake, Fivetran, and Looker. He previously worked in CRM and marketing analytics at Gopuff and in data science at Caesars Entertainment, and graduated from the Wharton School of the University of Pennsylvania with a BSE concentrating in Statistics and Operations, Information, and Decisions.

Analytics Engineering Lead

Navigating data consolidation after M&A

Participants in the dbt Labs partnership program are invited to a partner-only event on Monday, Oct. 17. More details coming soon!

Partner Day

Hackathon

Headline sponsor training

Headline Sponsor

Build foundational skills in dbt with this live course focused on models, sources, tests, docs, and deployment. More details coming soon!

Course 2: dbt Fundamentals live

Build an Open Lakehouse with dbt Labs and Dremio

Python model support is one of the hottest topics in the dbt Community Slack right now—but also one of the most intangible. What’s possible? What’s practical? What’s the point? In this session,  Eda Johnson (Snowflake) will show how Snowpark Python further enhances a dbt + Snowflake development experience by supporting new workloads, including Machine Learning within your dbt DAGs.

Python model support is one of the hottest topics in the dbt Community Slack right now—but also one of the most intangible. What’s possible? What’s practical? What’s the point? In this session, &nbsp;Eda Johnson (Snowflake) will show how Snowpark Python further enhances a dbt + Snowflake development experience by supporting new workloads, including Machine Learning within your dbt DAGs.

Eda Johnson is a Principal Data Cloud Architect and Global System Integrator (GSI) Tech Lead at Snowflake. She has 20 years of experience in architecting and implementing advanced analytics solutions in different industry verticals with an extensive knowledge in cloud architecture, data engineering and data science. In her current role, Eda works with our GSI Partners building solutions in the Snowflake Data Cloud.

Lead Partner Sales Engineer

Empowering pythonistas with dbt and snowpark

MDS Ecosystem

dbt Certification

Why Metrics Are Even More Valuable Than You Think They Are

The days of unrestrained cloud spending are coming to an end. In the coming months, data teams will increasingly be asked to better understand, monitor and reduce their data warehouse spend. The problem? Practitioners aren't equipped with the tools to do this well. In this talk, Ian Whitestone (Shopify) and Jonathan Talmi (Snapcommerce) will share their own end-to-end process for cutting warehouse spend, diving into the actual strategies they've deployed to give practitioners immediately actionable processes and techniques to keep costs controlled.

Ian’s been working in data for the past 6 years ever since abandoning a career in chemical engineering. He spent his first 3 years at Capital One, where he got to “play” in Teradata, Redshift and Snowflake while the company moved everything to the cloud. He spent the last 3 years at Shopify building and leading a full stack data science team, all while working closely with various business partners to drive value from data. You can learn more about him (https://ianwhitestone.work/about/), his work, and past talks here (https://ianwhitestone.work/talks/).

Jonathan has been working in data for the past 5 years. He’s worked on data teams at Shopify and Instacart, and currently leads a team of Analytics and Data Engineers at Snapcommerce. He’s been a passionate member of the analytics engineering community ever since he led the adoption of dbt at Snapcommerce 3 years ago, and his talk at Coalesce 2021, which showed how to add more observability to dbt, continues to generate a lot of in-bound interest today.

Data Science Lead

Extend the runway: A deep dive into data warehouse costs

Even though the lakehouse architecture offers many benefits to data teams looking at large-scale analytic workloads, it’s still relatively new to the dbt community. One of the main challenges of moving into this architecture has historically been ensuring source data is ingested and deduplicated on a data lake using updates, merges, transactions, and concurrency control mechanisms. In this session, Vinoth Govindarajan (Uber Technologies), will show how Apache Hudi and the newly developed dbt-spark adapter can bridge that gap, as well as how this architecture moved the latency of Uber’s petabyte-scale data pipelines from hours to minutes.

Staff Software Engineer

Petabyte-scale lakehouses with dbt and Apache Hudi

Escape from Data Island - Orchestrate and Connect Your Data Stack for Smooth Sailing

So often, data folks find ourselves solving the technical problems (how do we do X, how do we show Y), but forget that data is all about context. Effective use of our data tools goes beyond just picking the best one that has engineering best practices or the most cost effective tool. It is about understanding the people; the audience, the decision makers, the ones implementing the systems and so on. 

Join Lorena Vazquez (Wonder) as she shares tips in creating a more inclusive, compassionate, and empathetic data teams, and how that results in more successful projects.

So often, data folks find ourselves solving the technical problems (how do we do X, how do we show Y), but forget that data is all about context. Effective use of our data tools goes beyond just picking the best one that has engineering best practices or the most cost effective tool. It is about understanding the people; the audience, the decision makers, the ones implementing the systems and so on.&nbsp;Join Lorena Vazquez (Wonder) as she shares tips in creating a more inclusive, compassionate, and empathetic data teams, and how that results in more successful projects.

Lorena has worked in data for over 15 years. She is an accidental data person. Originally, a software engineer, she was introduced into Data Warehousing in the beginning of her career and hasn't looked back. She loves connecting people with data, improving data literacy at all ages, and reducing the gender gap in data. 
She currently leads the BI team at Wonder, a food tech startup changing the way people eat.

BI Lead

Empathy-building in data work

Welcome Happy Hour

"So what exactly do you do...?" As a data person, you may have been on the receiving end of this question before and that's because the value delivered by data teams is still not clear for many organizations. This makes it challenging to get appropriate investments for data teams to unlock value. Or on the other end of the spectrum, lack of scrutiny of data work can lead to major inefficiences. 

As data professionals, we should care about the value we create and evangelise our work better. The first step needed is a shared common language between data and its stakeholders. In this talk, Rifat Majumder (Education Perfect) explores a framework that he's been using with executives and business partners to convey the value delivered by data teams. Data leaders can use this to evangelize their team's work and get the right levels of investment they deserve.

"So what exactly do you do...?" As a data person, you may have been on the receiving end of this question before and that's because the value delivered by data teams is still not clear for many organizations. This makes it challenging to get appropriate investments for data teams to unlock value. Or on the other end of the spectrum, lack of scrutiny of data work can lead to major inefficiences.&nbsp;As data professionals, we should care about the value we create and evangelise our work better. The first step needed is a shared common language between data and its stakeholders. In this talk, Rifat Majumder (Education Perfect) explores a framework that he's been using with executives and business partners to convey the value delivered by data teams. Data leaders can use this to evangelize their team's work and get the right levels of investment they deserve.

Rifat has a varied background, but throughout his career, the common thread has been data. He has held roles in analysis, software, strategy and product. He is excited to see the progression of modern data team practices and technologies to help organisations leverage their data better and faster.

Data & Analytics Lead

Know your worth: Unpacking business value delivered by data teams

As your business and number of product lines grow, the out-of-the-box lead scoring in CRM tools starts becoming difficult to work with and lead scoring becomes that more important for sales teams. Join Ben Lewinsky as he shows how Culture Amp approaches multi-product lead scoring in their data warehouse using dbt.

As your business and number of product lines grow, the out-of-the-box lead scoring in CRM tools starts becoming difficult to work with and lead scoring becomes that more important for sales teams. Join Ben Lewinsky as he shows how Culture Amp approaches multi-product lead scoring in their data warehouse using dbt.

Experience in Rev Ops at multiple SaaS companies in Europe, North America and APAC

Director of Sales Operations.

Why you should not do lead scoring in your marketing automation tools

Without the right discipline and enablement, even a small data-informed startup can quickly become a complex maze of spreadsheets, queries, metrics and analysis. In an organization where access to relevant information is not prioritized, chaos will prevail. Come learn from Karen, a product manager at growing retail startup ALPHA Camp, share the insights she's earned from tackling this problem head on. In this session, she'll share effective strategies for building trust and partnering effectively across business and engineering functions, and how prioritizing structure and shared meaning is where it all starts.

Without the right discipline and enablement, even a small data-informed startup can quickly become a complex maze of spreadsheets, queries, metrics and analysis. In an organization where access to relevant information is not prioritized, chaos will prevail. Come learn from Karen, a product manager at growing retail startup ALPHA Camp, share the insights she's earned from tackling this problem head on. In this session, she'll share effective strategies for building trust and partnering effectively across business and engineering functions, and how prioritizing structure and shared meaning is where it all starts.

A product manager builds company-wide data literacy and empowers the product team to create values for people 👫 and grow 📈 the company to profit.

Director of Platform Product

How to build data accessibility for everyone

Just Getting Started

As organizations continue scaling their data investments, analytics practitioners are increasingly getting exposed to more areas of the business. In the process, they are learning that often times there are discrepancies in how key business metrics are being defined across teams. After encountering this first hand in her transition from business operations to product analytics, Nadja Jury (Education Perfect) became curious to learn how others were navigating this reality and set out to on a research mission. Nadja joins us at Coalesce to share her learnings from the field, and build more community around the conversation.

As organizations continue scaling their data investments, analytics practitioners are increasingly getting exposed to more areas of the business. In the process, they are learning that often times there are discrepancies in how key business metrics are being defined across teams. After encountering this first hand in her transition from business operations to product analytics, Nadja Jury (Education Perfect) became curious to learn how others were navigating this reality and set out to on a research mission. Nadja joins us at Coalesce to share her learnings from the field, and build more community around the conversation.

Hello! I'm Nadja, I'm an Analytics Engineer from New Zealand, my pronouns are She/Her, and I sit right on the Millennial/Gen Z cusp. I've been doing data-related work for the last 4 years, and I feel very lucky to have found a job I love and the supportive community that comes with it. I really enjoy the satisfaction that comes from working with data (most of the time). 

Outside of work I love baking and reading, and I'm both a cat and dog person.

Operations vs. product: The data definition showdown

Wellness Activity

The products that make up the “modern data stack” have all grown to prominence over the past decade. In this heady time, so much has changed about how data work is done.

But some of the “rules of engagement” that defined the original modern data stack are starting to break down. As a result, big changes are coming for the data tooling ecosystem.

The end result? Better, more integrated tooling, used by more humans inside of every company, that actually understands the data that it is operating on.

This modern data stack—if we still want to call it that!—will be unrecognizable to its former self.

The products that make up the “modern data stack” have all grown to prominence over the past decade. In this heady time, so much has changed about how data work is done.But some of the “rules of engagement” that defined the original modern data stack are starting to break down. As a result, big changes are coming for the data tooling ecosystem.The end result? Better, more integrated tooling, used by more humans inside of every company, that actually understands the data that it is operating on.This modern data stack—if we still want to call it that!—will be unrecognizable to its former self.

Tristan Handy is the Founder and CEO of dbt Labs, an emerging leader in the data transformation space used by 11,000 companies including JetBlue, HubSpot, Dunelm, and Vodafone New Zealand. Tristan has over two decades of experience as a data practitioner working in both large enterprises and startups. His expertise and data industry best practices have influenced thousands of subscribers and listeners weekly via his newsletter and podcast covering analytics engineering.

CEO and Founder

Chief Product Office 

Keynote: The End of the Road for The Modern Data Stack You Know

Learn how to answer your own data questions through dbt docs, the source freshness dashboard, and run results. More details coming soon!

Course 5: Disseminating knowledge with dbt Cloud

As analytics engineers and data people, we know the value we create in our own blood, sweat, and dbt models. But how is this value actually realized in practice? In this talk, David Jayatillake (Metaplane) draws on his experiences to discuss the processes, ways of thinking, tooling, and governance needed to realize the benefits from analytics engineering work in the greater organization.

As analytics engineers and data people, we know the value we create in our own blood, sweat, and dbt models. But how is this value actually realized in practice? In this talk, David Jayatillake (Metaplane) draws on his experiences to discuss the processes, ways of thinking, tooling, and governance needed to realize the benefits from analytics engineering work in the greater organization.

Head of Data 

The return on analytics engineering

Workshop

There’s no playbook for modern data leaders. As a community, we’re still figuring out what it means to be a great data leader—how to build a great culture, what tools and frameworks to use, what stack to build, etc. For someone starting new in this role, it can be overwhelming. 

In this candid chat moderated by Christine Garcia (Atlan), data leaders will unpack how they think about: 

- Investing in building a strong foundation for your data team: What it looks like and how they approached this for their own teams 

- Nurturing a growing team: How data leaders can help their team productivity and people grow while continuing to meet the growing demands from the rest of the organization 

- Cultivating a data culture unique to your data team: Setting and building the right team values, approaching governance inside the team, creating cadences and rituals, and more

There’s no playbook for modern data leaders. As a community, we’re still figuring out what it means to be a great data leader—how to build a great culture, what tools and frameworks to use, what stack to build, etc. For someone starting new in this role, it can be overwhelming.&nbsp;In this candid chat moderated by Christine Garcia (Atlan), data leaders will unpack how they think about:&nbsp;- Investing in building a strong foundation for your data team: What it looks like and how they approached this for their own teams&nbsp;- Nurturing a growing team: How data leaders can help their team productivity and people grow while continuing to meet the growing demands from the rest of the organization&nbsp;- Cultivating a data culture unique to your data team: Setting and building the right team values, approaching governance inside the team, creating cadences and rituals, and more

Director of Content

The secrets of a modern data leader: The first 365 days inside a data team

When does your team decided it’s time to move beyond a singular `dbt run`? For most analytics engineers, there comes a time when e dbt run commands on fixed schedules simply won’t make the cut.   Join Prratek Ramchandani (Vox Media) as he breaks down an alternative approach to orchestrating your dbt project with Dagster that balances meeting SLAs with safely handling the edge cases a simple schedule-based `dbt run` might create.

When does your team decided it’s time to move beyond a singular `dbt run`? For most analytics engineers, there comes a time when e dbt run commands on fixed schedules simply won’t make the cut.   Join Prratek Ramchandani (Vox Media) as he breaks down an alternative approach to orchestrating your dbt project with Dagster that balances meeting SLAs with safely handling the edge cases a simple schedule-based `dbt run` might create.

I'm the Engineering Manager for the Data team at Vox Media and really enjoy reasoning about the experience of building on the modern data stack.

Outgrowing a single `dbt run`

Business metrics are at the core of Slido’s analytics and reporting. But challenges with rapid data exploration and metrics-related workflows have led Slido through evolution of different approaches to reporting. This talk describes Slido’s journey to better reporting— covering the technical and organizational challenges associated with creating reliable insights across tools and teams. The talk finishes with a look at the latest iteration of Slido’s reporting architecture, highlighting how Deepnote’s and dbt Cloud them supercharge their exploratory workflows and deliver world-class reporting.

Business metrics are at the core of Slido’s analytics and reporting. But challenges with rapid data exploration and metrics-related workflows have led Slido through evolution of different approaches to reporting. This talk describes Slido’s journey to better reporting— covering the technical and organizational challenges associated with creating reliable insights across tools and teams. The talk finishes with a look at the latest iteration of Slido’s reporting architecture, highlighting how Deepnote’s and dbt Cloud them supercharge their exploratory workflows and deliver world-class reporting.

Driving reporting excellence at Slido with Deepnote

In a popular Coalesce 2020 talk, Dave Fowler challenged the relevancy of dimensional modeling and the star schema popularized by Ralph Kimball over 25 years ago, arguing the dimensional modeling method now does “more harm than good,” in the context of the modern data stack. But should we throw the baby out with the bathwater? In his talk, Josh Devlin (Brooklyn Data Co) suggests a different approach to bringing Kimball’s system into the modern era.

Josh loves designing data models and then using SQL to wrangle raw data to match his designs. When he’s not elbow deep in SQL, you can find him in the dbt Slack, answering questions and working on his knowledge of dbt.

Babies and bathwater: Is Kimball still relevant?

Two teams of three data analysts compete in a series of challenges in this ultimate showdown. Who will reign supreme? 

Hosted by Claire Carroll and Izzy Miller from Hex.

Two teams of three data analysts compete in a series of challenges in this ultimate showdown. Who will reign supreme?&nbsp;Hosted by Claire Carroll and Izzy Miller from Hex.

Legends of the Hidden Table

The year is 2022, data teams are earning their stripes, data work is being valued, and our organizations recognize that analytics engineering is no longer the future of data teams—it’s the present. Join Mong Dang as she discusses how Aritzia, a normal company with a talented and growing roster of data team members, scaled up their delivery of quality data through analytics engineering and leveraged their data product management approach. Like all experimental work, Mong will cover what they tried, what has been working, what didn’t work, and where they still have questions and can learn more on.

The year is 2022, data teams are earning their stripes, data work is being valued, and our organizations recognize that analytics engineering is no longer the future of data teams—it’s the present. Join Mong Dang as she discusses how Aritzia, a normal company with a talented and growing roster of data team members, scaled up their delivery of quality data through analytics engineering and leveraged their data product management approach. Like all experimental work, Mong will cover what they tried, what has been working, what didn’t work, and where they still have questions and can learn more on.

As a champion for increasing the depth of knowledge in the practice of analytics engineering, Mong is proudly part of Aritzia, a Canadian design house and fashion boutique, as the Analytics Engineering Director. 
Her team is dedicated to transforming data into clean, reliable data sets for analysis and applying software engineering best practices, like version control and continuous integration, to analytics code and data pipelines.
She also serves on the board of Chic Geek, a nonprofit that creates a welcoming, supportive community for women in technology.
She was trained in Computer Systems Engineering and has a Master’s Degree focused on predictive analytics and 3D visualization with imaging informatics for non-invasive medical applications. She went through a diverse path of leading teams and strategic transformations in six different industries to enable actionable insights from data and technology assets.

Analytics Engineering Director

Unlocking analytics engineering at scale

Data suggests that occupational identity has consequences for work engagement which in turn influences turnover intention. In a “new normal” with hybrid work environments and ever-increasing employee turnover, it’s crucial that our employees are engaged and feel a sense of belonging. As a data team manager, Kasey Mazza knows how important it is to build a community of data-minded folks that gives your people a place to learn, grow, and thrive. 

Join Kasey as she discusses the things they’ve done at HubSpot to build a sense of community and camaraderie both within her team and across their entire data community of ~400 people. This talk will highlight: 

- Why diversity matters 

- Living your values 

- Leading with curiosity and empathy 

- Creating intentional space 

- Reducing or avoiding burnout

Data suggests that occupational identity has consequences for work engagement which in turn influences turnover intention. In a “new normal” with hybrid work environments and ever-increasing employee turnover, it’s crucial that our employees are engaged and feel a sense of belonging. As a data team manager, Kasey Mazza knows how important it is to build a community of data-minded folks that gives your people a place to learn, grow, and thrive.&nbsp;Join Kasey as she discusses the things they’ve done at HubSpot to build a sense of community and camaraderie both within her team and across their entire data community of ~400 people. This talk will highlight:&nbsp;- Why diversity matters&nbsp;- Living your values&nbsp;- Leading with curiosity and empathy&nbsp;- Creating intentional space&nbsp;- Reducing or avoiding burnout

Passionate about data-driven decision making, process efficiency, data visualization and storytelling, high-performing inclusive teams, behavioral economics, Diet Coke, and pasta in all forms. Currently managing a team of Analytics Engineers at HubSpot.

Analytics Engineering Manager

How to build your data team like a community

Analytics Engineer, currently based in The Hague, NL

Data as a Product: How dbt powers Slido’s Data API

Welcome to the dbt Package show! Have you ever wondered how Fivetran approaches building dbt packages? Well, put on your aprons, because the Fivetran team is going to give you a glimpse at their recipe for building packages with dbt from 0 -> release -> implementation. Sheri Nguyen of Fivetran will show you: 

- How they leverage the dbt Community to get the right ingredients for our packages and plan our roadmap for end state package models 

- Their methodologies and standards for preparing ingredients for our data models to provide flexibility for all our users 

- How they combine everything together and cook up our models for our dbt packages 

Finally, after all the cooking is done, the best part is sharing what you've made with your friends and family. Sheri will also show you how different companies use our various packages and how you can contribute! This talk will benefit those who want to leverage Fivetran's dbt packages, understand why we apply certain modeling practices, and understand how we continually iterate on these packages. Additionally, this session will be beneficial for those who also want to be dbt package maintainers and understand how they can contribute future packages.

Welcome to the dbt Package show! Have you ever wondered how Fivetran approaches building dbt packages? Well, put on your aprons, because the Fivetran team is going to give you a glimpse at their recipe for building packages with dbt from 0 -&gt; release -&gt; implementation. Sheri Nguyen of Fivetran will show you:&nbsp;- How they leverage the dbt Community to get the right ingredients for our packages and plan our roadmap for end state package models&nbsp;- Their methodologies and standards for preparing ingredients for our data models to provide flexibility for all our users&nbsp;- How they combine everything together and cook up our models for our dbt packages&nbsp;Finally, after all the cooking is done, the best part is sharing what you've made with your friends and family. Sheri will also show you how different companies use our various packages and how you can contribute! This talk will benefit those who want to leverage Fivetran's dbt packages, understand why we apply certain modeling practices, and understand how we continually iterate on these packages. Additionally, this session will be beneficial for those who also want to be dbt package maintainers and understand how they can contribute future packages.

Sheri was born and raised in the Bay Area. She holds a B.S. in Mathematics and Economics as well as a M.S. in Data Science, both from the University of San Francisco. She worked in data science for five years at a variety of Bay Area tech companies focusing on product and fraud detection. She made the change into analytics engineering last fall with Fivetran.

Senior Analytics Engineer

Mastering the art of dbt package development

There’s no “one-size-fits-all” when it comes to one’s career journey. This workshop will give you a framework to refactor and/or develop your data career path, from data engineers to business analysts, individual contributors to managers. We’ll outline a structure for unique career path development. This workshop will benefit individual contributors who work for rapidly growing companies and want help managing up, and managers who want to further their career while also building career paths for their teams!

There’s no “one-size-fits-all” when it comes to one’s career journey. This workshop will give you a framework to refactor and/or develop your data career path, from data engineers to business analysts, individual contributors to managers. We’ll outline a structure for unique career path development. This workshop will benefit individual contributors who work for rapidly growing companies and want help managing up, and managers who want to further their career while also building career paths for their teams!

Clearing the path: a career progression framework for individual growth

Workshop Sponsor

Analytics at a startup….something that might scare most data folks. But not Lindsay Murphy of Maple! Join Lindsay as she draws on her experiences to demystify and template the process behind using dbt to  create robust self-service analytics practices at startups and other small companies.

Analytics at a startup….something that might scare most data folks. But not Lindsay Murphy of Maple! Join Lindsay as she draws on her experiences to demystify and template the process behind using dbt to &nbsp;create robust self-service analytics practices at startups and other small companies.

I've been in the analytics industry for 10+ years and have grown from a data coordinator to a director leading data teams. I'm passionate about solving challenging problems, building successful teams, and data storytelling.

Director of Data and Analytics 

“The easy way” to launch analytics at a startup with dbt

Ecosystem Sponsor w/ Atlan 

Data Apps in the Real World: How to Capture Value Locked in the Data Warehouse

Roleplaying games? Roleplaying games in data? You read that right. To Ian, there's more commonality in roleplaying games and the data world than most of us think. In this session, Ian Fahey (dbt Labs) will draw on his vast experiences in roleplaying games and analytics engineering work to walk through the adventuring classes of "the world's most popular tabletop roleplaying game" (Dungeons and Dragons) and talk about how they can inform data professionalism.

Roleplaying games? Roleplaying games in data? You read that right. To Ian, there's more commonality in roleplaying games and the data world than most of us think. In this session, Ian Fahey (dbt Labs) will draw on his vast experiences in roleplaying games and analytics engineering work to walk through the adventuring classes of "the world's most popular tabletop roleplaying game" (Dungeons and Dragons) and talk about how they can inform data professionalism.

Staff Analytics Engineer in the DC tech space (and new dbt Data team member) obsessed with producing a data ecosystem that inspires stakeholders

Identified the brightest spots in a twelve-year prior life as an actuary and realized they were all driven by data

What classes from roleplaying games can teach us about a career in data

dbt Labs

dbt makes easy things easy and hard things possible. But there’s a few things that you really shouldn’t try if you value your time—and sanity. In this light-hearted session, Randy Pitcher (dbt Labs) will share some of the most common (and egregious) dbt workarounds that can tank your project, productivity, and pride.

dbt makes easy things easy and hard things possible. But there’s a few things that you really shouldn’t try if you value your time—and sanity. In this light-hearted session, Randy Pitcher (dbt Labs) will share some of the most common (and egregious) dbt workarounds that can tank your project, productivity, and pride.

Randy is a Solutions Architect at dbt Labs.

Solutions Architect

But I won't do that — things you shouldn't do with dbt

There's a kind of funny (and painful) paradigm that exists in the data world: more data, more problems. As companies grow their data, there's more broken dashboards to find, impacted users to chase, and cobbling data together that happens. But what if there was tooling to make the scaling process a little less painful, and a lot more scalable? 

Join Marion Pavillet (MUX)  and Kevin Hu (Metaplane) as they share stories around scaling data stacks at a large and small companies, demonstrate how they used modern tools to help improve data lineage and accuracy, and discuss the power of metadata in improving data products over time. Attendees can expect to leave this session with new concepts they can use to frame problems faced by rapidly growing companies, a blueprint for implementing DataOps, and solutions to which they can map their own situations.

There's a kind of funny (and painful) paradigm that exists in the data world: more data, more problems. As companies grow their data, there's more broken dashboards to find, impacted users to chase, and cobbling data together that happens. But what if there was tooling to make the scaling process a little less painful, and a lot more scalable?&nbsp;Join Marion Pavillet (MUX) &nbsp;and Kevin Hu (Metaplane) as they share stories around scaling data stacks at a large and small companies, demonstrate how they used modern tools to help improve data lineage and accuracy, and discuss the power of metadata in improving data products over time. Attendees can expect to leave this session with new concepts they can use to frame problems faced by rapidly growing companies, a blueprint for implementing DataOps, and solutions to which they can map their own situations.

I studied languages and business but got into tech a few weeks into my first job. 
A few years later, I started working at a cloud based data visualization tool that was then acquired by Zendesk. 
I kept pursuing the 'data' side and joined the internal data team.

I am passionate about data and making it available to everyone. 
In this world, I also appreciate the fast pace and how the data ecosystem/landscape keeps changing!

Sr Analytics Engineer

More metadata, less problems

In this session, Guillermo Sanchez, (GoDataDriven) will attempt to summarize the last four major socio-technical changes that data teams have faced in the last few years, and why dbt plus the highly contentious data mesh might be the answer to each.

My name is Guillermo Sanchez, I an 28 years old and I come from Madrid, Spain. Currently I am based in Amsterdam, where I work as an Analytics Engineering Tech Lead for a boutique consultancy called GoDataDriven.

Although I originally studied industrial engineering, I quickly change interests to computer science. I did backend development for a while, but quickly realized I was interested in the data world. My interest at first was focused on Machine Learning, but again I quickly discovered that many companies where struggling to make their data available in the first place, and could see myself adding a lot more value as an Analytics/Data Engineer. 

Being my background a bit more technical, I do not only work with dbt at clients, but also build applications/platforms from scratch, including CI/CD, Infrastructure as Code, etc. The part that I enjoy the most is designing architectures that will help companies achieve value from data.

In my personal life I stop being a tech geek and go climbing quite often (currently a lot of indoor bouldering since Amsterdam does not have a damn rock). I also love reading (anything really) and hanging out with friends outdoors when there's an inch of sun in this country (which doesn't happen often).

Analytics Engineering Tech Lead

dbt & data mesh: the perfect pair (?)

*Insensatez* in Portuguese means folly, unwisdom. In other words, doing something you deep down know you shouldn't. Data practitioners don't always have the right incentives or orgizational influence to advocate for the solutions that they believe are best fit for the problems they are solving. Join Felippe Felisola Caso (Loft) as he bravely wades into the awkward and often unspoken feelings between leaders and analysts. Through an exploration of organizational feelings and perspectices, attendees can expect to walk away with a greater capacity for empathy, as well as proven strategies for improving communication and building alignment.

*Insensatez* in Portuguese means folly, unwisdom. In other words, doing something you deep down know you shouldn't. Data practitioners don't always have the right incentives or orgizational influence to advocate for the solutions that they believe are best fit for the problems they are solving. Join Felippe Felisola Caso (Loft) as he bravely wades into the awkward and often unspoken feelings between leaders and analysts. Through an exploration of organizational feelings and perspectices, attendees can expect to walk away with a greater capacity for empathy, as well as proven strategies for improving communication and building alignment.

Facilitator, musician and engineer; a dog person, and aspiring barman. Passionate about leadership, self-knowledge and technology. Helps companies and people to create knowledge from data, currently leading part of the Analytics team at Loft, Brazilian startup working to bring the home buying experience to the e-commerce age. Teaches and mentors students in career development and data analysis.

Analytics Manager

How insensitive: Increasing analytics capacity through empathy

Ecosystem talk w/ Mode Analytics 

Lineage is a critical component of any root cause, impact analysis, and overall analytics heath assessment workflow. But it hasn’t always been easy to create, particularly at the field level. In this session, Mei Tao, Helena Munoz, and Xuanzi Han (Monte Carlo) tackle this challenge head-on by leveraging some of the most popular tools in the modern data stack, including dbt, Airflow, Snowflake, and ANother Tool for Language Recognition (ANTLR). Learn how they designed the data model, query parser, and larger database design for field-level lineage—highlighting learnings, wrong turns, and best practices developed along the way.

Mei Tao is a product manager at Monte Carlo, a data reliability company. Prior to joining Monte Carlo, Mei worked in product management at NEXT Trucking and Product Strategy at Coinbase. She received her MBA from Harvard Business School and her B.S. in Statistics from the University of California, Berkeley.

Senior Product Manager

Field-level lineage with dbt, ANTLR, and Snowflake

“Why didn’t my job run?” 

“What happened to this dashboard?” 

“Why is this column missing?” 

“Why is my data… wrong?!” 

If you’ve been on the receiving end of a broken data pipeline, these questions probably look familiar to you. And as data ecosystems become increasingly distributed and complex, the likelihood of this “data downtime,” or in other words, periods of time where data is missing, inaccurate, or otherwise erroneous, only grows. 

Drawing corollaries to application downtime and the world of software engineering, data downtime can be addressed through a robust approach to developing, testing and observability. Just as an engineer would never deploy code to production without testing it first, or run production software without application performance monitoring and observability, analytics engineering teams can apply a similar “testing + observability” framework to their data pipelines. 

In this demo presentation, Monte Carlo’s Jon So will walk through how best-in-class analytics engineering teams at JetBlue, HubSpot, Vimeo, and other companies solve for data quality at scale with dbt for testing and Monte Carlo for end-to-end data observability.

“Why didn’t my job run?”&nbsp;“What happened to this dashboard?”&nbsp;“Why is this column missing?”&nbsp;“Why is my data… wrong?!”&nbsp;If you’ve been on the receiving end of a broken data pipeline, these questions probably look familiar to you. And as data ecosystems become increasingly distributed and complex, the likelihood of this “data downtime,” or in other words, periods of time where data is missing, inaccurate, or otherwise erroneous, only grows.&nbsp;Drawing corollaries to application downtime and the world of software engineering, data downtime can be addressed through a robust approach to developing, testing and observability. Just as an engineer would never deploy code to production without testing it first, or run production software without application performance monitoring and observability, analytics engineering teams can apply a similar “testing + observability” framework to their data pipelines.&nbsp;In this demo presentation, Monte Carlo’s Jon So will walk through how best-in-class analytics engineering teams at JetBlue, HubSpot, Vimeo, and other companies solve for data quality at scale with dbt for testing and Monte Carlo for end-to-end data observability.

Achieving Data Observability at Scale with dbt and Monte Carlo

Allowing more people into the data development process can improvement shipment speed, but can also cause some anxiety for folks that wonder how to preserve best practices as participation expands. In his session, Kshitij Aranke (Vouch Insurance), shares how his team created safe inroads to communal development through automated guardrails on dbt projects that provided automatic best practice checks on each pull request.

Kshitij Aranke is a Senior Data Analytics Engineer at Vouch Insurance.

Agenda