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Engineering & IT · Chicago, IL
Data Engineer jobs in Chicago, IL
Data engineers build the pipelines and warehouses that move data from source systems to the people and models that need it, keeping it fresh, correct, and queryable at scale.
606
Open roles today
$40–$101/hr
Typical pay range
$139k
Median, full-time
5
Fresh in this list
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01
Open data engineer roles
11 shown of 606 · sorted by freshness
$145k - $165k
in your work as we create a better future together.OverviewAs a Lead of Analytics Engineering at Avison Young Technologies, you will lead the strategy and execution of our proprietary data products and models that empowe…
$125k - $140k
Mesirow is looking for a partner to support and modernize our data architecture with the vision of establishing a modern data estate... ...a cloud-native platform built on Azure, Databricks and modern engineering practic…
$156k - $234k
Dir Data Engineering - GE06AEWe’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals…
Managing Director, Data Engineering & AIWho You'll Work WithAs a Managing Director in Slalom's Data & AI practice, you will lead the growth of our Data Engineering & AI business by helping clients modernize their data fo…
Position: Data Engineer (Healthcare Exp Is Must) Location: St. Louis MO Richardson TX Chicago IL (Onsite) Duration: Contract Job Description We are looking for an experienced Data Engineer with strong expertise in Databr…
$179.4k - $204.7k
Overview Data Engineer 4 Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive and iterative delivery environment? At Capita…
$70 per hour
job summary: Are you a Data Engineer with over 10 years of experience and expertise working with Databricks on AWS? Overview: · We are investing in the next evolution of our data, analytics, and artificial intelligence e…
Responsibilities: Design and develop scalable data pipelines for data extraction, transformation, integration, and loading.... ...techniques. Collaborate with data architects, data scientists, AI engineers, and analysts…
Are you looking for an exciting new opportunity? Join a world-class trading firm where some of the brightest traders, engineers, and researchers collaborate to solve one of the most challenging problems in global markets…
Job Summary We are looking for a skilled Data Engineer specializing in Graph Databases (Neo4j) to design and maintain robust data pipelines and model complex data structures. The ideal candidate will be responsible for c…
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What data engineers earn in Chicago
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $40–$56 | $84k–$117k |
| Mid level | $56–$78 | $117k–$162k |
| Senior | $75–$101 | $157k–$211k |
Adjusted for the Chicago market from national ranges.
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What employers ask for
The skills these listings keep naming
SQL and data modelingPython or ScalaAirflow or similar orchestrationSpark or other batch processingWarehouses (Snowflake, BigQuery)dbtStreaming (Kafka)Data quality testingCloud infrastructure
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Interview questions worth rehearsing
With the thing the interviewer is actually listening for
Design a pipeline that loads data from a production database into a warehouse daily.
Cover extraction strategy, incremental loads versus full refresh, idempotency, and monitoring. Saying how you would backfill after a failure shows real pipeline experience.
How do you handle late-arriving or duplicate data?
Discuss idempotent upserts, watermarks, and dedup keys. This is a daily reality of the job, so a concrete example lands well.
Batch or streaming — how do you decide?
Anchor on the actual freshness requirement and cost. Most 'real-time' asks are fine at minutes; recognizing that is the mature answer.
A stakeholder says the numbers in their dashboard are wrong. Walk me through your debugging.
Trace lineage from the dashboard back to the source, isolating which layer diverged. Showing calm, structured lineage-tracing is the point of the question.
How do you model data for analytics — star schema, wide tables, something else?
Show you know the classic patterns and modern warehouse economics, and that you choose based on query patterns and team skill, not doctrine.
How do you test data pipelines?
Talk about schema and freshness checks, row-count and distribution tests, and tools like dbt tests or Great Expectations — plus alerting when they fail.
Tell me about a pipeline that failed badly and what you changed.
Structure it like an incident review: impact, cause, fix, prevention. Emphasize the durable improvement, such as monitoring or contract enforcement.
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Resume tips that move the needle
For data engineers specifically — generic advice costs you here
01
State data scale plainly — rows per day, terabytes managed, pipeline counts — because it is the first thing hiring managers scan for.
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Name your orchestration, processing, and warehouse tools per role; the modern stack (Airflow, dbt, Snowflake) is a keyword screen.
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Highlight reliability outcomes: pipeline failure rates, data freshness SLAs met, incident reductions.
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Show cost work if you have it — warehouse spend is a live concern and optimization stories differentiate.
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Mention who consumed your data (analysts, ML teams, executives) to show you build for users, not just movement.
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Where this role goes
Typical progression
01
Junior Data Engineer
02
Data Engineer
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Senior Data Engineer
04
Staff Data Engineer
05
Data Platform Lead
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