$148k - $185k
where all team members belong and have the opportunity to thrive. Data Science is at the heart of Lyft's products and decision-making.... ...enterprise partners. You'll collaborate closely with Product, Engineering, Desi…
Engineering & IT · San Francisco, CA
Data Engineer jobs in San Francisco, CA
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.
543
Open roles today
$52–$131/hr
Typical pay range
$181k
Median, full-time
6
Fresh in this list
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01
Open data engineer roles
12 shown of 543 · sorted by freshness
$177.19k - $364.8k
more about our AI interview philosophy and how we use AI in our recruiting process here.We are looking for an Analytics Engineering Manager II, Core Data Science to help improve the quality and velocity of data science a…
$186k - $230k
will be assessed during the interview process.About the Role:As a Data Scientist supporting Risk, you will play a crucial role in... ...you’ll be working with an established team and risk leaders in Engineering, Product,…
in any country where we have a legal entity.Atlassian’s Trusted Data Platform (TDP) provides secure, reliable, and scalable data-... ...is Atlassian’s managed relational database platform. It gives engineering teams a su…
$159.2k - $301.6k
About the roleAdobe’s Security Data Platform team builds and operates a petabyte-scale security data lakehouse that turns enterprise... ...investigations, compliance, and security analytics.As a Staff Security Data Engin…
$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…
new job opportunity. If are you Comfortable with Below Position then please share me your updated resume. Sr. Data Scientist/Data Engineer SF/Bay Area OR Seattle/Bellevue, WA(Hybrid) Long Term Contract JD MUST HAVES: BS…
Senior Data Engineer Databricks / Spark / AI Location: San Francisco, CA Work Model: Hybrid 2 Days/Week Onsite Duration: 12+ Months Employment Type: Contract Experience: 5+ Years Work Authorization: Visa-independent cand…
ABOUT THE ROLE As our first dedicated Data Engineer, you will build and own the data foundation that powers analytics, reporting, and decision-making across the organization. This is a hands-on role where you'll design t…
$150k - $200k
Our client, a growing AI and Data organization, is hiring a Founding Data Engineer to join the team. The successful candidate will help transform enterprise data into actionable intelligence by building the backend syste…
$190k - $220k
as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process. The Data Engineering team builds tools and systems that make Gusto's data consistent, user-friendly, a…
$190.8k - $238.8k
offices in New York, Washington D.C., London and Amsterdam. Making data- driven decisions is key to Plaid's culture. To support that, we... ...data. We provide golden datasets and tooling to teams across engineering, pro…
02
What data engineers earn in San Francisco
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $52–$73 | $109k–$151k |
| Mid level | $73–$101 | $151k–$210k |
| Senior | $98–$131 | $203k–$273k |
Adjusted for the San Francisco 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.
05
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.
03
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.
05
Mention who consumed your data (analysts, ML teams, executives) to show you build for users, not just movement.
06
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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