Data Engineer vs Analytics Engineer: What's the difference?

Since I started my Analytics Engineer traineeship at Nimbus Intelligence, I noticed that this emerging role is often confused with the Data Engineer role.

Photo source: Analytics Engineer: Job Description, Skills, and Responsibilities

Both Data Engineers and Analytics Engineers are essential in data teams, but their objectives and expertise differ in subtle ways:

Data Engineer: Focuses on building infrastructure for data generation, conversion, and storage. They create and maintain the architecture (like databases) and pipelines to funnel data to the data scientists.
Analytics Engineer: Transforms data to make it useful for analysis. They ensure that data is easily accessible and work to improve data quality.

In this article, I’ll briefly explain what are the differences between Data Engineers and Analytics Engineers.

Differences between a Data Engineer and an Analytics Engineer. In a table.

As I’m getting used to structuring data as tables, I’ll do the same for this blog post.

	Data Engineer	Analytics Engineer
Main Objective	Build and maintain the infrastructure for data generation, conversion, and storage.	Make data accessible and usable for analysts and data scientists through transformation and modeling.
Primary Tools	Hadoop, Spark, Kafka, Airflow, BigQuery, AWS tools (Redshift, S3, etc.)	Snowflake, SQL, dbt (data build tool), Looker, Tableau, Redshift.
Key Activities	Data ingestion, setting up data pipelines, data warehousing, data optimization.	Data transformation, building data models, creating views and dashboards.
Collaboration with other roles	Work closely with data scientists to ensure data is available and in the right format.	Collaborate with data analysts, data scientists, and occasionally with data engineers.
Required skill set	Strong programming (Python, Java), big data technologies, ETL tools, database systems.	Strong SQL skills, understanding of data warehousing concepts, basic programming (often Python or R).
Data infrastructure management	Primary responsibility to manage and optimize.	Relies on infrastructure established by data engineers but may have a say in its design or modifications.