When people think about data jobs, they usually think of one of three roles: data engineering, data analytics or data science. But there’s another growing field that’s in-between two of these, one that I’ve been learning about.
But first, let’s define these three roles. While there’s a lot of other data jobs like data architects and AI engineers we don’t be going into these today.
Three Main Data Roles
At a high level, the data workflow looks like this:
Data Engineer → Builds the infrastructure and pipelines.
Data Analyst → Analyzes and interprets the data.
Data Scientist → Uses models to predict and optimize outcomes.
For more details on these see my blog post: The 3 Main Data Roles: Data Engineer, Data Analyst, and Data Scientist.
Overview
Role | Data engineer | Data Analyst | Data Scientist |
Focus | Infrastructure and data pipelines. | Generating insights from data. | Uses models to predict and optimize outcomes. |
What they do | Build ETL/ELT pipelines, ensure data accessibility, optimize databases, work with big data tools | Analyze datasets, create dashboards, identify trends, support business decisions | Develop machine learning models, conduct exploratory analysis, focus on predictive analytics |
Example Task | Creates pipelines to move data from APIs to cloud storage. | Builds dashboards to monitor KPIs. | Builds a model to predict customer churn. |
Skills | software engineering knowledge, Python, Java, Knowledge of big data tools and distributed systems, ETL | SQL, data visualization (e.g. Tableau, PowerBI), EDA, Excel, strong domain knowledge | machine learning, statistical analysis, Python |
What’s Analytics Engineering?
Analytics engineering focuses on transforming raw data into a structured, analysis-ready state. This role is relatively new and emerged as teams needed someone who understands both data engineering and data analytics. It essentially bridges the gap between the two.
What’s a Analytics Engineer Do?
Focus: Transforming raw data into clean, usable datasets so it’s easier to analyze.
What they do: Build and maintain data models, transform data for analysis, and enforce data quality best practices.
Responsibilities
Develop and maintain data models (e.g., in dbt).
Collaborate with analysts to ensure data meets business needs.
Implement best practices for data quality, governance, and testing.
Bridge the gap between data engineers and analysts.
What’s the difference between a data engineer and an analytics engineer?
While both roles work with data transformation, they focus on different things:
Data Engineer | Analytics Engineer |
Builds and maintains data infrastructure | Models, cleans, and organizes data for analysis |
Designs ETL/ELT pipelines and manages big data tools | Uses pipelines to transform data into a usable format |
Works with distributed systems, databases, and cloud storage | Ensures analysts can easily access, query, and use data |
Optimizes data storage and retrieval performance | Optimizes query efficiency and usability |
Think of it like this: - Data Engineers ensure data is collected and stored efficiently. - Analytics Engineers ensure that data is structured and accessible for analysis.
Skills Needed for Analytics Engineers
Strong SQL & data modeling (Kimball, star schemas).
Experience with dbt, Snowflake, BigQuery, or cloud data platforms.
Understanding of data governance, testing, and best practices.
Focus on data transformation and usability
Overlap
There’s a lot of overlap between the data engineer, analytics engineer and data analyst. Some analytics engineers might do deep dives into analysis like a data analyst, while others focus on writing production level Python code. In some companies, an analytics engineer might do tasks that look more like a senior data analyst or a data engineer “lite”, but it depends on the team.
Conclusion
Analytics engineering is in-between data engineering and analytics, making sure that raw data is transformed into a structured format that analysts and business teams can use. It’s a growing field that blends technical skills with business understanding (my favorite part about data), making data more accessible and valuable.
Personally, it’s a field I’m excited about. I’m in the process of transitioning it myself. More on that in a future blog post.
Sources
Below are the resources I used for this blog post: