A leading financial technology company is seeking a Senior Manager in Software Engineering to oversee the Data and Storage Services team in Portland, Oregon. The ideal candidate will lead engineers to design and implemen…
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.
169
Open roles today
$41–$103/hr
Typical pay range
$142k
Median, full-time
2
Fresh in this list
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01
Open data engineer roles
8 shown of 169 · sorted by freshness
$85k - $116.8k
-effective and transformative projects to the electric vehicle, data center, consumer goods and products, metals, pulp and paper and... ...location. We're looking for a high-energy, Mechanical HVAC Engineer to focus on o…
required for this role ~ AWS, Databricks certifications nice to have, not required ~8-10 years' experience minimum preferred in a data- related role ~ Prior Nike experience required ~ Databricks Spark Python Snowflake SQ…
Work options: Hybrid Mon-Thurs onsite. Typical Title: Data Engineer | Machine Learning Engineer Locations : Beaverton, OR | Boston, MA As a Data Engineer contractor at Nike, you can do work with impact. To be considered…
$77.18k - $88.2k
Title: Data Analyst Location: River House - 211 SE Caruthers St, Portland OR 97214 Schedule: Hybrid (onsite REQUIRED; local candidates only), Mon-Fri 8:00am-4:30pm (schedule flexibility available) Wage Range: $77,175 - $…
continue leading us to greatness. The next tastemakers, playmakers, risk takers and glue players. Are you game? LEAD, DATA ENGINEER - NIKE [Beaverton, OR - USA] WHO YOU'LL WORK WITH Consumer Product and Innovation (CP&I)…
Their expertise spans enterprise networking, cloud, cybersecurity, data centre technologies and managed services, delivering tailored... ...and prepared for future growth. Taking a consultative, engineering- led approach…
$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…
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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.
03
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.
02
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.
04
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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