The world of data is evolving, and companies are increasingly looking for professionals who combine analytical and engineering skills. That’s exactly how the role of Data Analytics Engineer emerged – a hybrid of a Data Engineer and a Data Analyst.
But what does this person actually do? What are their responsibilities? And is this the future for data professionals?
Here are 5 key areas where a Data Analytics Engineer adds value:
1. Builds and Optimizes Data Pipelines – Focused on Analytics
A Data Analytics Engineer doesn’t build massive enterprise data warehouses like a classic Data Engineer but focuses on creating and maintaining data pipelines tailored to analytical needs.
💡 What does that mean?
- Working with tools like SQL, dbt, and Airflow to transform and model data.
- Optimizing ETL/ELT processes so that analysts have access to clean, up-to-date data.
- Collaborating with analysts and business teams to ensure data is not only accessible but also usable.
📌 Example:
A company wants to analyze daily sales across multiple countries. A Data Analytics Engineer creates a pipeline that pulls data from various systems, processes it, and delivers clean tables to analysts.
2. Builds the Analytical Layer – Modeling with SQL and dbt
In traditional roles, Data Engineers prepare raw data, and Data Analysts clean and model it. The Data Analytics Engineer optimizes this middle layer.
💡 What does that mean?
- Designing clean, well-structured analytical tables that are easy to use in BI tools and dashboards.
- Optimizing SQL queries for performance and speed.
- Standardizing data definitions, such as KPIs, metrics, and dimensions across the company.
📌 Example:
Instead of having each analyst write their own SQL queries, the Data Analytics Engineer builds predefined views and data models in dbt, which the whole team can use.
3. Automates Reporting and Analysis
A Data Analytics Engineer doesn’t create dashboards, but they ensure that reporting is automated, reliable, and easy to maintain.
💡 What does that mean?
- Automating refresh cycles in BI tools (Power BI, Looker, Tableau).
- Writing scripts to detect data issues and send alerts.
- Integrating tools to ensure data from different systems is consistent and centralized.
📌 Example:
If a Power BI sales report breaks every week due to bad data loads, the Data Analytics Engineer builds an automated system with alerts to catch issues before analysts notice.
4. Optimizes Query Costs and Performance
In large organizations, every query can have a cost, and inefficient SQL can slow systems down. The Data Analytics Engineer takes care of performance and cost optimization.
💡 What does that mean?
- Improving slow SQL queries to make them faster and cheaper.
- Using materialized views, indexes, and other optimization strategies.
- Monitoring resource usage to identify where cloud costs (e.g., in Snowflake, BigQuery, Redshift) can be reduced.
📌 Example:
A marketing report takes 30 minutes to load? The Data Analytics Engineer reviews the queries, implements indexing, and cuts the runtime to seconds – saving both time and money.
5. Bridges the Gap Between Business and Tech
This is one of the biggest differences between a Data Engineer and a Data Analytics Engineer – the latter needs much more business awareness.
💡 What does that mean?
- Understanding what data the business actually needs, not just managing pipelines.
- Working with analysts and managers to deliver insights, not just raw data.
- Ensuring data quality and alignment – like defining unified KPIs across the company.
📌 Example:
The sales department shows different numbers across tools? The Data Analytics Engineer creates a single source of truth, defining KPIs consistently so that everyone sees the same data.
Summary – Is It Worth Becoming a Data Analytics Engineer?
💡 Is this the right role for you? ✅ You enjoy working with data but want to impact real analysis,
✅ SQL is your second language and you like building solid pipelines,
✅ You want to grow not just technically, but also on the business side,
✅ You care about optimization, automation, and enabling better reporting.
📌 Is this the future of data roles? Yes! More and more companies are looking for professionals who combine analytics with engineering.
It’s a natural next step between a Data Analyst and a Data Engineer.
If you want to stay ahead in the data job market – this is a high-potential direction for your career!
Other interesting articles:
- How to Get Your First Job as a Data Analyst During a Market Slowdown – 5 Smart Strategies
- 5 Mistakes That Can Get You Fired as a Junior Data Analyst (and How to Avoid Them)
- AI and the Future of Data Analysis: Will AI Replace Data Analysts?
Prefer to read in Polish? No problem!
The article was written by Kajo Rudziński – analytical data architect, recognized expert in data analysis, creator of KajoData and polish community for analysts KajoDataSpace.
That’s all on this topic. Analyze in peace!
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