The Data Career Compass (3/8): Analytics Engineer & dbt hacks with Panagiotis Apostolopoulos
The Analytics Engineer is a new role at the intersection of data engineering and data analytics. In practical terms, this means they own the 'T' in ELT.
Hello Antonis here!
In this blogpost we’ll explore the role of Analytics Engineer. Our podcast guest is a former colleague of mine, Panagiotis Apostolopoulos, Analytics Engineer at Hack The Box, a great guy and professional and I can admit that I cannot be biased with him because we are connected in many ways, from bugs, sql models and dirty infra work to conferences around the world. Thanks Panos for supporting DataConscious and let’s return to the role!
The Analytics Engineer is a new role at the intersection of data engineering and data analytics. It has emerged as a response to modern data workflow challenges and opportunities and it continues to evolve. At its core, the analytics engineer is responsible for ensuring the quality, reliability and usability of data across an organization. The role sits at the heart of the operation, connecting raw data infrastructure to trusted analytics ready data that business users can confidently explore.
In this episode of DataConscious – A Mindful Approach to Analytics, we explore the ever-evolving role of the Analytics Engineer. Our guest, Panos, brings clarity to this hybrid position that lives between data engineering and data analytics, offering a practical view on what it really means to enable data driven decision making from within.
🎙 What exactly is an Analytics Engineer? From owning data transformation pipelines to bridging business needs with technical solutions.
🎙 His path into the role: Shares his transition into the role.
🎙 A day in the life: You’ll hear how he manages dbt projects, curates business logic, ensures data quality and collaborates closely with both business stakeholders and data engineers.
🎙 Skills that stand out: From SQL and data modeling to the art of documentation and stakeholder communication, he reveals which capabilities have made the biggest difference in his effectiveness.
🎙 Where the field is going: As the role matures, we discuss the shifting expectations, the impact of modern data stacks and how analytics engineers are becoming cornerstone members of data teams.
🎙 Advice for newcomers: Panos offers insights on learning SQL deeply, embracing version control, thinking like a software engineer, and finding joy in iteration and testing.
Whether you're curious about this emerging role or aiming to pivot into it, this episode is packed with grounded insights and actionable advice. The Analytics Engineer isn't just a technical translator, it’s someone who ensures the business sees the data clearly, consistently and confidently.
Whether you're a data leader, engineer, BI professional or decision-maker, this episode will help you navigate the evolving landscape of modern data architectures.
🎧 Listen now & subscribe for more insights on mindful data practices!
(Please find the 2025 State of Analytics Engineering Report which Panos mentions in the podcast episode at the end of the post)
What Does an Analytics Engineer Do?
Analytics engineers wear many hats in a data team. Some of their core day-to-day responsibilities include:
🏗️ Building & Transforming Data Models: Analytics engineers spend a lot of time writing SQL queries to transform raw data into structured tables. They take messy, siloed data from various sources and model it into clean, unified datasets. This often means applying business rules (e.g. renaming fields, cleaning formats, calculating new metrics) so that the data makes sense for analysis. The goal is to create a single source of truth that analysts and business folks can trust instead of everyone fetching raw data.
🧪 Ensuring Data Quality (Testing): Because they treat data like a product, analytics engineers set up tests and monitoring to ensure data is accurate and reliable. For example, they might write tests to catch if yesterday’s sales data suddenly dropped to zero or if there are duplicate entries where there shouldn’t be. Using tools like dbt, they can build tests right into the transformation process (think of it like unit tests for data 🙌). If something looks off, they investigate and fix it before it misleads someone.
🚦 Monitoring Data Pipelines: Analytics engineers also keep an eye on data pipelines and workflows. They make sure all the automated jobs that move and transform data (often running overnight) have completed successfully. If a pipeline fails or data is delayed, the analytics engineer gets an alert and jumps in to troubleshoot. In this way, they ensure data is flowing from point A to point B on time and nothing critical breaks without them knowing.
🔧 Optimizing & Refactoring: With a foot in software engineering, an analytics engineer continuously improves the code and queries that shape the data. They review each other’s SQL code, refactor messy or slow-performing queries, and tune the data models for efficiency. This might mean rewriting a heavy query to run faster, or reorganizing a data model for easier use. Over time, these tweaks keep the data playground running smoothly and scalable as data grows.
📝 Documenting & Defining Data: Analytics engineers often act as data librarians for the organization. They document what each data model or table means, define business terms (e.g. what exactly counts as an “active user”?) and make sure everyone can find and understand the datasets available. This might involve writing clear descriptions in a data catalog or within their dbt project so that anyone (from a new analyst to the CEO) can grasp what the data represents.
🛠 Skills - Tools - Key Responsibilities
To accomplish all of the above, analytics engineers rely on a mix of modern tools and software best practices. Here are some of the key ones:
SQL: SQL is the alphabet for an analytics engineer. It’s the main language they use to query and reshape data in the warehouse. Strong SQL skills are a must, as they’ll be writing complex joins, aggregations and data cleaning logic every day.
dbt (Data Build Tool): dbt has become the go to tool for analytics engineering. It allows them to manage all those SQL transformations as modular code. With dbt they organize transformations into models (SQL files) and can run them in sequence ensuring dependencies are handled (e.g. build raw data into staging tables then into intermediate tables then final reports). dbt also enables writing tests, documenting models and even creating a lineage graph so you can see how data flows from raw sources to final analytics. It’s like an analytics engineer’s command center for building a robust analytics layer on top of the raw data.
Version Control (Git) and Code Review: Just like software engineers, analytics engineers use Git or other version control to collaborate on code. All the SQL scripts and configuration (often in a dbt project) live in a repository. They create branches to add new features or fix issues and teammates review each other’s code changes. This process helps catch mistakes early and maintain a high quality for the data transformations (no more “quick and dirty script” living on someone’s laptop — everything is tracked 👍).
Orchestration & Scheduling: Tools like Apache Airflow, dbt Cloud or other workflow orchestrators are used to schedule and run data pipelines. Analytics engineers set up these schedules so that, say, every morning the latest data is loaded and transformed by 7am. They also handle dependency management and alerts if a data source didn’t refresh or a transformation fails, the orchestrator will alert the team. Essentially, they make sure the whole system runs perfectly.
Data Warehousing & Cloud Platforms: Analytics engineers work closely with data warehouses (like Snowflake, BigQuery, or Redshift) where all the data lives. They need to be familiar with how to optimize queries for these platforms and use their features (partitioning, indexing, etc.) effectively. They also often leverage cloud services for storage and processing. While deep infrastructure might be more the data engineer’s realm, analytics engineers should know enough to make the most of their warehouse and not, say, run a query that costs a fortune or slows everything down.
Data Quality & Observability Tools: Beyond just writing tests in dbt, many analytics engineers implement additional tools to monitor data quality and pipeline health. This can include packages like dbt-expectations (for extended testing) or standalone tools like Great Expectations and Monte Carlo for data observability. These help catch anomalies or bad data early. Some teams also track metrics like how fresh the data is, or if any dashboards are broken, to proactively maintain trust in data. The following chart of 2025 State of Analytics Engineering Report illustrates it better.
How to proactively grow into an Analytics Engineer role?
So how can you become an analytics engineer, or sharpen the skills if you’re aiming for this role? Here are a few tips to get started:
👉 Master SQL fundamentals: SQL is your best friend. Practice writing queries across different scenarios – joining tables, filtering, aggregating, etc. You can start with any sample database (like PostgreSQL or even an online SQL tutorial) to get comfortable. The better you get at SQL the easier everything else becomes.
👉 Learn a transformation tool like dbt: Since dbt is so central to analytics engineering today, it’s worth getting hands-on experience with it. You can take a public dataset (for example, COVID-19 data or Spotify song data) and try building a mini dbt project. This will teach you how to structure models in layers (staging → intermediate → reporting), how to write tests, and how to use Jinja (dbt’s templating) for DRY (Don’t Repeat Yourself) code. There are many community tutorials and even a dbt online course for beginners – take advantage of those.
👉 Develop your data modeling mindset: Read up on the basics of data modeling (things like star schema vs. 3NF, fact and dimension tables) and practice by designing schemas for problems you’re interested in. Think about how raw data should be organized to answer real business questions. For example, if you love sports, imagine how you’d model player and game stats for analysis. Modeling is part art, part science —> the more you do it, the better you get.
👉 Embrace testing and data quality checks: Start treating your data work like software development. Learn about data quality tools and why they matter. This mindset of “trust but verify” will set you apart as someone who delivers reliable results, not just any results.
👉 Build a portfolio project: Nothing beats real-world practice. Create a project where you take raw data, load it (you could use an open-source pipeline tool or even manual CSV import), transform it with dbt or SQL, and then analyze it or build a small dashboard. Focus on showcasing your thinking: document the problem you tackled, how you modeled the data, how you ensured quality and what insights came out. Even if it’s a simple project, this is a great way to demonstrate to potential employers that you understand end-to-end analytics engineering. Plus, you’ll learn a ton by doing it.
👉 Sharpen soft skills (communication & business understanding): An often overlooked aspect of this role is the ability to communicate and understand context. Practice explaining your data findings or project decisions in plain language. This could be through blogging, or just talking to non-technical friends about your project. Also, train yourself to always ask “what is the business need here?” whenever you tackle a data task. The more you tune into the actual problem to solve (e.g. increasing user engagement, reducing costs, etc.) the more effective your solutions will be.
👉 Connect with the data community & keep learning: The analytics engineering space is evolving, so stay curious! Join communities like the dbt Slack or relevant forums/subreddits, follow data blogs or a Substack (😉), attend virtual meetups or local data events. Learning from others can spark ideas and keep you up-to-date on new tools and best practices. And don’t be afraid to ask questions or even contribute to open-source projects. Being active in the community can accelerate your growth and also connect you with mentors or job opportunities.
Remember, becoming a great analytics engineer is a journey. It blends technical stuff with a keen understanding of people and business. So keep experimenting, keep learning, and don’t hesitate to think outside the box. Every data problem you solve (or even attempt) is a step forward. Good luck on your analytics engineering adventure! 🚀
Maggie explains it also quite well…
📈 Career growth opportunities and salary expectations
Analytics engineers command impressive salaries, reflecting their pivotal role in bridging data engineering and analytics.
Entry-Level Positions: Starting salaries range from $110,000 to $117,000 annually.
Senior-Level Roles: Salaries can escalate to $190,188 per year, with top-tier professionals earning up to $627,000, particularly in high-demand markets or leading tech companies.
Notable Example: At Netflix, analytics engineers have reported median salaries of $197,371, with compensation packages reaching up to $295,194.
These figures underscore the lucrative nature of the analytics engineering profession, especially for those who continuously enhance their skills and adapt to emerging technologies.
The role of an analytics engineer is not only financially rewarding but also offers diverse pathways for professional advancement. Professionals in this field can progress into roles such as:
Data Architect: Designing and managing complex data systems.
Analytics Manager: Leading teams to derive actionable insights from data.
Product Data Lead: Overseeing data strategies for product development.
Chief Data Officer (CDO): Setting the data vision and strategy at the organizational level.
The demand for analytics engineers spans various sectors including technology, finance, healthcare, and e-commerce. The projected growth rate for this profession is 18% over the next decade highlighting its critical importance in data-driven organizations.
In a nutshell…
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Thanks for reading until here. I know this one was a bit big :)
References
https://www.getdbt.com/resources/state-of-analytics-engineering-2025
https://www.talent.com/salary?job=and+analytics+engineer
https://www.businessinsider.com/how-much-netflix-pays-employees-us-salary-levels
https://www.datacamp.com/blog/what-is-an-analytics-engineer-everything-you-need-to-know
https://discourse.getdbt.com/t/snowflake-dbt-how-to-use-different-role-in-model-view/8156
https://www.reddit.com/r/dataengineering/comments/1fvtlq9/what_is_analytical_engineer/
https://www.coursera.org/articles/analytics-engineer
https://www.getdbt.com/blog/what-is-analytics-engineering
Great engineering starts with great awareness. Be mindful, be DataConscious.
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