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Engineering & IT · Minneapolis, MN
Data Engineer jobs in Minneapolis, MN
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.
181
Open roles today
$38–$96/hr
Typical pay range
$132k
Median, full-time
12
Fresh in this list
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01
Open data engineer roles
12 shown of 181 · sorted by freshness
$93.1k - $122.5k
500 per year Requirements: We prefer a bachelors degree in Computer Science, Information Systems, Data Analytics, Management Information Systems, Data Engineering, or a related discipline. We require at least 4 years of…
Role: Data Engineer Location: Minneapolis, MN Work from office 3 days We are looking for an experienced Data Engineer to design, build, and maintain scalable data pipelines and infrastructure on Azure , leveraging Databr…
$45 - $55 per hour
DescriptionKforce has a client that is seeking a hybrid Senior Data Engineer to join their growing team in Minneapolis, MN. This team is focused on the -ethics, compliance, HR and legal- data aspect of the client and in…
Job-ID29238416Reference26-25469Seeking a Principal Microsoft Data & AI Engineer who combines advanced SQL and Microsoft Fabric expertise with hands-on data engineering, data wrangling, analytical modeling, and practical…
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…
Data Engineer Location: Minnesota (MN) Job Description: We are seeking an experienced Data Engineer with strong expertise in Python, PySpark, SQL, and Databricks to design, develop, and optimize scalable data pipelines a…
$92.3k - $138.5k
Mortenson is currently seeking a Data Scientist that will be responsible for modeling complex business problems and discovering business... ...44 days. ABOUT MORTENSONAs a top builder, developer, and EPC ( Engineering, P…
$132.26k - $155.6k
all from Day One.Job DescriptionJob Duties -Responsible for big data/ analytics projects that gather and integrate large volumes of... ...in a quantitative field such as statistics, computer science, engineering or appli…
$85.6k - $149.4k
Informaticist combines clinical expertise, healthcare informatics, and data science to support the development, implementation, and... ...Sentri7 Drug Diversion platform. Working closely with product, engineering, data s…
to join a retail organization on a Contract basis in Bloomington, Minnesota. This position focuses on turning marketing and customer data into actionable insights through AI-driven analysis, predictive modeling, and perf…
Job-ID29338947Reference26-26568Required Qualifications • Strong SQL and data analysis skills. • Experience working with large-scale data warehouses and analytics platforms. • Experience with GCP data environments, includ…
02
What data engineers earn in Minneapolis
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$53 | $80k–$110k |
| Mid level | $53–$74 | $110k–$153k |
| Senior | $71–$96 | $148k–$199k |
Adjusted for the Minneapolis 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
04
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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