Chicago, IL
Senior Data Engineer jobs in Chicago, IL
4 open roles from 4 employers. Checked Oct 6, 2026.
What these openings look like
4 employers are hiring here: Conagra Brands, SpotHero, Tempus AI and Belvedere Trading. 3 of the 4 publish a pay range, running $82,000 to $200,000 a year. The rest do not say. Every posting is full time. The most recent went up on Sep 25, 2026.
Every listing links to the employer's own application. Nothing is posted here on their behalf, and nothing is reposted from anywhere else.
Open roles
What it pays here
Only some employers publish a range. These are the ones that did, as written in their own postings:
- $82,000–$120,000 a year
- $136,000–$153,000 a year
- $150,000–$200,000 a year
What they ask senior data engineer candidates here
Drawn from the 4 open postings on this page. The ones that come up across several employers are near the top.
Walk me through a pipeline you built in Databricks with PySpark that lands data in Snowflake. What were the hard parts?
What they are checking: Real hands-on experience with the exact stack named, including how they handled scale and failures.
How do you design a pipeline so it can be rerun safely after a failure without duplicating data?
What they are checking: Whether they understand idempotent loads, merge logic and checkpointing.
A business user says the numbers on a dashboard you built don't match finance's report. How do you track it down?
What they are checking: Whether they trace definitions, filters and source timing systematically and fix the root cause.
How would you set up data quality checks on a pipeline feeding the Data Science team?
What they are checking: Whether they build automated tests for freshness, completeness and valid ranges, with alerting.
Tell me about a time you translated a vague data request into clear engineering requirements.
What they are checking: Whether they ask the right questions to understand the real business need before building.
How would you help grow self-service analytics without ending up with a mess of inconsistent reports?
What they are checking: Whether they think about curated data models, shared definitions and governance to make self-service safe.
How do you tune a slow Spark job?
What they are checking: Practical knowledge of partitioning, shuffles, skew, caching and file sizes.
When would you model data as a star schema versus a wide flat table for reporting?
What they are checking: Whether they understand tradeoffs in usability, performance and maintenance for BI consumers.
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