$60k - $100k
Salary: $60,000 - 100,000 per year Requirements: Strong background in Python, SQL, and core data engineering practices Hands-on experience building ETL/ELT solutions Practical knowledge of Apache Spark, PySpark, or Datab…
Engineering & IT · Pittsburgh, PA
Data Engineer jobs in Pittsburgh, PA
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.
317
Open roles today
$37–$91/hr
Typical pay range
$125k
Median, full-time
2
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01
Open data engineer roles
11 shown of 317 · sorted by freshness
value integrity and collaboration, you'll feel at home at McNees. McNees, Wallace & Nurick is actively seeking a Data Platform & Integration Engineer to join our Information Technology department. At McNees, we believe o…
$130k - $140k
Job Title: Industrial Engineering Analytics Engineer Location: Pittsburgh, PA (Onsite) Role Overview: The Industrial Engineer... ...Operations, Supply Chain, Finance, and Engineering teams to support data- driven decisio…
Pittsburgh, PA (Hybrid) Classification: Exempt Status: Full-Time Reports to : Senior Director, Data & Analytics Purpose The Manager, Analytics Engineering leads the Analytics Engineering track within the Data & Analytics…
Skills & Experience • Strong hands-on experience with Snowflake (tables, views, performance tuning) • Advanced SQL skills and data transformation expertise • Experience building data ingestion pipelines (ETL/ELT) • Exper…
$70.8k - $156.7k
Data Engineer Position Description We are seeking a Data Engineer with 5 years of experience to design and maintain scalable data pipeline supporting analytics, reporting, and operational needs. The role involves collabo…
Data Engineer – Azure Databricks Contract-to-Hire Pittsburgh, PA – Onsite Job ID J0726-0569 Visa : USC, GC, EAD (No Sponsorship) Position Overview Seeking an experienced Data Engineer to join a high-performing data engin…
optimization Simulation tools (FlexSim / AnyLogic / Simio) Data analytics: Excel (advanced), SQL, Python, BI tools (Power BI/... ...analytics (preferred) Must-Have Experience: 7+ years in industrial engineering analytics…
Position Title: Data Engineer Location: PA – Pittsburgh (Locals Preferred) Work Status : Onsite 5 days a week Duration : Contract to Hire Years Of Experience Required : 6+ Years Industry Background: Finance / Banking Fun…
Data Ideology At DI, we provide Data & Analytics expertise to drive measurable business outcomes, often solving complex business... ...curious. For more information about Data Ideology, visit Sr. Data Engineer - (Snowfla…
$79.6k - $139.3k
Data Engineer - Databricks - Local to Pittsburgh, PA Position Description Join a high performing data engineering team responsible for building modern, cloud native data platforms on Microsoft Azure. This role offers the…
02
What data engineers earn in Pittsburgh
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $37–$50 | $76k–$105k |
| Mid level | $50–$70 | $105k–$146k |
| Senior | $68–$91 | $141k–$189k |
Adjusted for the Pittsburgh 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
03
Senior Data Engineer
04
Staff Data Engineer
05
Data Platform Lead
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