Coalesce 2022 Replay Tracks: For the Emerging Analytics Engineer

Coalesce wrapped last week with more than 120 community-led sessions covering the newest tools and tactics in data. While many speakers had deep experience in analytics engineering and dbt — most attendees did not!
In fact, only half of all Coalesce registration emails are tied to dbt Slack accounts, and of that group, 70% only joined in the last year! It’s no wonder that “Zero to dbt” drew the largest attendee count—second only to the keynote. So, if you’re new to this space, you’re in good company! But if you’re looking to fast track your progression, this collection of talks (plus bonus resources!) is a great place to start.
Below you’ll find three “tracks” to help you understand the analytics engineering workflow, build foundational frameworks, and assemble your stack.
Module 1: Role and workflow understanding (2 hrs)
Still have questions about what analytics engineering actually is, and how to communicate it to others? Start here. This module is especially useful for members of the data team looking for a crash course in what it might take to redefine their roles.
- I don’t build models, I construct knowledge (24 mins)
- Engineering your analytics career path (15 mins)
- What classes from roleplaying games can teach us about a career in data (28 mins)
- Analyst to Analytics Engineer (19 mins)
- The accidental analytics engineer (27 mins)
Looking for more help? Check out these resources:
- The Analytics Engineer, by Michael Kaminsky
- What is analytics engineering, by Claire Carroll
- How to find a role in analytics engineering, by Danielle Leong
- Spinning up an analytics engineering team, by Monzo
Module 2: Building foundational frameworks (2 hrs 45 mins)
If you’re ready to bring analytics engineering to your team (even if you’re a team of one!) this module will show you have to develop baseline skills, frameworks, and best practices to ensure success at scale.
- But really, what is transformation? (30 mins)
- Testing: Our assertions vs. reality (28 mins)
- Minimum viable (data) product (32 mins)
- The “easy way” to launch analytics at a startup with dbt (26 mins)
- How to leverage dbt Community as the first & ONLY data hire to survive (27 mins)
- Jumpstart dbt: How to Achieve Speed and Scale (25 mins)
Looking for more help? Check out these resources:
- SQL: The Video Game, Joe Markiewicz, Coalesce 2022
- The Analytics Engineering Guide, many contributors, curated by dbt Labs
- Scaling data analytics with software engineering best practices, by Nubank
Module 3: Choosing your tooling (3 hrs minus workshops)
You’re ready to get moving, but there’s one more set of decisions you’ll need to make. Which tooling will support the structure you’ve established for your team? Or alternatively, which tooling will change how you’ve thought about structuring your team? This module includes talks that shed some light on several layers of the modern data stack.
- Building a Data Platform from Scratch with dbt, Snowflake and Looker (27 mins)
- Zero to dbt (workshop: 2 hrs)
- Developing on dbt Cloud (42 mins)
- Make Analysts Love You: How Acorns simplifies their data pipelines with Rudderstack and dbt Labs (21 mins)
- Move Fast and (Don’t) Break Things: Testing dbt Models with Bigeye (27 mins)
- Escape from Data Island - Orchestrate and Connect Your Data Stack for Smooth Sailing (26 mins)
- Modern Data Management: how to setup your data for success (29 mins)
- Introducing dbt with Databricks (Workshop: 1 hr)
- Moving to predictive: How to assemble the beginnings of your feature store with Snowflake & dbt Labs (Workshop: 47 mins)
Looking for more help? Check out these resources:
- dbt Fundamentals (Free guide on using dbt Cloud)
- The modern data stack: Past, present, and future, by Tristan Handy
- The beginner’s guide to the modern data stack, by Prukulpa
Phew! That was a lot, but there’s still many more sessions waiting for you on the dbt YouTube channel, if you’re keen to keep going!
Last modified on: Dec 6, 2023