Data Engineer 4 jobs in Chicago, IL

The Marketing and Messaging team sends hyper-personalized messages across owned and paid Adtech channels. How would you design a pipeline that feeds customer data into those messages reliably?

What they are checking: Whether they can design for freshness, scale and correctness in a pipeline that touches customers directly.

Walk me through a Spark job you tuned for performance. What was slow and what did you change?

What they are checking: Whether they understand partitioning, shuffles and skew from real experience rather than theory.

What does a lakehouse architecture give you over a separate data lake and warehouse, and where does it struggle?

What they are checking: Whether they understand the trade-offs of the architecture the team uses, not just its name.

How would you use GenAI in a data engineering workflow here, and what controls would you put around it?

What they are checking: Whether they can apply GenAI usefully while respecting data privacy in a regulated bank.

A marketing message went out to customers who had opted out. You suspect a data issue. What do you do first?

What they are checking: Whether they act quickly to stop harm, find the root cause and think about compliance impact.

How do you build data quality checks into a pipeline so bad data never reaches downstream teams?

What they are checking: Whether they treat validation and monitoring as part of the build, not something added later.

Tell me about a time you influenced how other engineers or data scientists built something, without being their manager.

What they are checking: Whether they can lead technically through influence, which this level expects.

When would you choose a NoSQL database over a relational one for a messaging platform?

What they are checking: Whether they choose storage based on access patterns and scale rather than habit.