Data Analyst to Analytics Engineer
How to make the transition and sell yourself to a future employer
Am I technical enough? Do I have enough experience with tools like Snowflake, dbt, and Airflow? Will data engineers take me seriously? Will I enjoy working heads-down on data models?
These are the questions many data analysts wonder when considering analytics engineering as their next career step.
The truth is, data analysts make some of the best analytics engineers. They have stakeholder communication nailed down, which is often the hardest skill to learn. They already know SQL. They’ve been on the opposite end of the producer/consumer relationship, so they are already aware of common pitfalls.
If you, as a data analyst, are confident in your ability to communicate and prioritize, there is nothing you can’t do. You just need to get yourself up to speed with the technical skills required to be a great analytics engineer.
In this newsletter, we’ll talk:
How to know if analytics engineering is right for you
The technical skills you need to learn
How to sell yourself on your resume, or during an interview
This week’s newsletter is sponsored by Data Expert.io, a data engineering academy created by
to help take your data engineering career to the next level. Zach’s analytics engineering boot camp will begin on April 14th.In this boot camp, you will cover:
8 hours on dbt
8 hours on Trino, Snowflake, and Iceberg
Mastering 5 analytics interviews
Capstone project
I joined Zach’s data engineering boot camp last fall to deepen my engineering knowledge and finished with a ton of insight based on his hands-on experience at Facebook, Netflix, and Airbnb. If you’ve been considering a program like this, now is a great time to join! Use code LEARNAE2025 for 25% off.
How to know analytics engineering is the next best step
It’s scary to pivot in your career and make a change. I’ve been there. I discovered analytics engineering after a few years working as a data engineer, realizing I needed something more business-focused. At first, I didn’t know if I was making the right decision, but as soon as I went for it, I was confident it was the best one for me.
During my time as a data engineer in a 10,000+ person company, I felt:
unfulfilled in the work I was doing
declining desire to learn new things
yearning for something else
If you feel the same way I did, it’s probably time to try something new.
Analytics engineering is a great choice for data analysts who want to focus more on technical skills like data modeling, data warehousing, and building data pipelines. It’s ideal if you want to dip your toe into data engineering but enjoy improving business processes and working with stakeholders.
With analytics engineering, you get the business exposure but aren’t necessarily at the beck and call of stakeholders. Instead, you get to focus on creating scalable solutions using the business data generated.
Analytics engineering is right for you if:
✅ You are constantly wishing you were the one creating datasets.
✅ You are frustrated with the data given to you to use in dashboards and reports.
✅ You want to interact with engineering, ensuring data is produced as expected.
✅ Interacting with the business is one of your favorite parts of being a data analyst.
How to prepare to make the jump
The hardest part about making the jump is just doing it. The skills that are the hardest to learn on your own, you already have! It’s really just a few technical skills that you need to take the time to develop.
Data warehousing
Data warehouses are your central hub to store data from different sources, allowing you to query them all in one place. You can think of it as the one place you look to when you need to confirm or compare numbers. As an analytics engineer, you own this source of truth.
While every data warehouse setup has unique benefits, it’s important to understand basics like materializations and data governance, as these are key for managing both costs and security within a data warehouse.
The most common data warehouses in the modern data stack include Snowflake, Redshift, Databricks, and BigQuery. Starting with any one of these will set you up for success. Just be sure to focus on the main principles and best practices rather than getting too in the weeds on how to use the specific tool.
📖 Check out How to Setup a Data Warehouse for Analytics for a step-by-step guide on creating schemas, users, and data governance strategies in Snowflake.
Applying SQL skills to a tool like dbt
If you’re already a data analyst, you are most likely familiar with window functions, complex joins, and calculating business KPIs. Now you just need to understand how to use this same logic to build modular data models. This can be done using a data transformation tool like dbt, which helps enforce best practices.
Using dbt, you can create core models and reusable macros, all while taking advantage of built-in testing and data quality packages.
Data modeling
As an analytics engineer, you are expected to create data models that allow data analysts to easily answer questions asked by business stakeholders. This requires an understanding of concepts like facts and dimensions, star schemas, slowly changing dimensions, normalization, and granularity.
I’ve found this to be another difficult skill to learn outside of my day-to-day work. If you have the chance, ask an analytics engineer you currently work with to show you some of the data models they’ve built. Ask them why they made certain design decisions to help get a feel for what you need to consider when building them.
📖 Check out 7 Data Modeling Concepts You Must Know for an introduction on what to study.
Data quality best practices
Because you are “shifting left” from a data analyst to an analytics engineer, you are now one step closer to the production of data. This means you are responsible for ensuring the data that makes it to the analysts is of the highest quality.
You need to understand how to best communicate with engineering when the data doesn’t meet your expectations. Because, let’s be honest, it often won’t.
You need to check for duplicate values, inconsistent data types, and freshness. Think of yourself as the gatekeeper of the data! You only want to make the data you’ve validated and put your seal of approval on available downstream.
Data quality best practices can consist of adding tests, defining contracts, assigning owners, and documenting how to use the data.
📖 For learning how to test your data using dbt, check out The Easiest Way to Test Data Quality.
Selling yourself
Many people are afraid they don’t have the experience they need for a role, and this lack of confidence shows in interviews. Remember, your experience as a data analyst is your experience as an analytics engineer. The experience of one can absolutely be transferred to the experience of the other.
As a former data analyst, you have a leg up in understanding how to communicate with data analysts and ask them what they need. You understand them in a way nobody else does!
You know exactly the questions to ask to help inform your data model design decisions. You can anticipate future blockers based on hurdles you’ve faced in the past while working with analytics engineers.
Make sure you emphasize all of these things on your resume and in interviews! This is your superpower. Embrace it and harness it to communicate your value as an analytics engineer.
You know how to talk to the business better than anyone else!
For more on interviewing, check out How to Master the SQL Technical Interview.
Now get studying and apply to some jobs!
Madison
PS: Don’t forget to check out DataExpert.io for the analytics engineering boot camp launching next week and use code LEARNAE2025 for 25% off!