$71.3k - $140.6k
Position Summary Our Deloitte AI & Engineering team works to transform technology platforms, drive innovation, and help make a significant... ...fuel growth through innovation. Work you'll do As a PROJECT - Data Engineer…
Engineering & IT · Portland, OR
Data Engineer jobs in Portland, OR
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.
144
Open roles today
$41–$103/hr
Typical pay range
$142k
Median, full-time
5
Fresh in this list
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01
Open data engineer roles
10 shown of 144 · sorted by freshness
needs. This in-office requirement may be adjusted at the discretion of the company.Columbia Sportswear Company is seeking a Senior Data Engineer in Portland, OR responsible for designing and implementing Kimball-style di…
$114k - $142k
-provider outages, and building the self-service tooling other engineering teams use to adopt fine-grained authorization for their own services... ...teams and internal stakeholders manage and troubleshoot user data and…
$122k - $145k
acceptance criteria* Create process maps that demonstrate a client’s business workflows to assist in stakeholder alignment* Capture data, reporting, security, and user experience needs at a functional level* Validate req…
$78k - $105k
Streaming Data Engineer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic o…
Job Title: Sr. Data Engineer Location: Portland, OR Onsite Job Type: Contract Experience: 14+ Years Mandatory Interested candidates are required to include their LinkedIn URL, email address, and cell phone number along w…
$78.91k - $110.27k
diverse experiences and perspectives help us become a stronger organization. Let’s be better together. Position Summary The Data Engineer role on the Data Science Team (DST) is responsible for designing, maintaining, and…
a technology company. From our flagship website and five-star mobile apps to developing products, managing big data, and providing leading edge engineering and systems support, our teams at NIKE Global Technology exist t…
and passion to a challenging and constantly evolving game. Data science is a competitive differentiator for Nike and is fundamentally... ...direction, guide standard methodologies in data science and engineering, and hel…
hurricanes, the primaries and more. Founded by 20-year veterans of the cyber security industry, Swan Island Networks began as a software engineering lab working with the US government, focusing on R&D programs. Now our p…
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What data engineers earn in Portland
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $41–$57 | $86k–$119k |
| Mid level | $57–$79 | $119k–$165k |
| Senior | $77–$103 | $160k–$215k |
Adjusted for the Portland 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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