Job Title: Data Engineer Location: Los Angeles, CA (Remote/Hybrid) – Locals preferred or anywhere in the US Duration: 6+ months Contract MOI: Phone/Video (MSTeams) Job Description: Python Mongo SQL Server Excellent commu…
Engineering & IT · Los Angeles, CA
Data Engineer jobs in Los Angeles, 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.
180
Open roles today
$46–$114/hr
Typical pay range
$157k
Median, full-time
7
Fresh in this list
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01
Open data engineer roles
11 shown of 180 · sorted by freshness
only in your community, but around the world. We believe building engineering is more than systems and structures, it’s about powering... ...where people work, connect, and thrive. From high-performance data centers driv…
extraction required where no standard interface exists.Canonical Data Model and Schema Governance: Own the schemas, tag dictionaries,... ...and data requirements; flow them down to OEMs and to internal engineering; verif…
Data Engineer Direct Hire Onsite Pasadena (local are encouraged to apply) W2 Only (No Sponsorship/C2C) We are looking for a Data Engineer to design, optimize, and manage scalable data architecture. In this role, you will…
$110.4k - $165.6k
both for our audiences and our employees – and aim to leave a positive mark on culture. In This Role You’ll: The Senior Data Engineer should possess a deep sense of curiosity and a passion for building data pipelines, da…
$98.4k - $147.6k
moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture. Overview The Data Engineering team is seeking a Data Engineer – Self-Service Analytics & Real-Time Data Platfo…
$98.4k - $147.6k
we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture. Data Engineer – Data Pipeline & ETL 46034 Overview and Responsibilities Job Summary The Data Engi…
Role : Sr. Data Engineer Location: Pasadena, CA Work Arrangement: Hybrid Job Summary We are looking for an experienced Data Engineer with strong expertise in Databricks, PySpark, and Python to design, develop, and mainta…
Job Overview We are seeking a driven and detail-oriented Data Engineer to build, maintain, and optimize our data infrastructure and processing pipelines. In this role, you will be responsible for designing scalable data…
partner with leading studios, broadcasters, and media companies to deliver technology, data, and digital transformation solutions. The Role We are seeking an experienced Analytics Engineer for a 4–6 month project support…
$125k - $159k
culture at the forefront. What You’re Applying For: The Data Platform team manages, supports, and enhances the system that... ...Edmunds’ business intelligence. Our user base consists of analysts, engineers, business own…
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What data engineers earn in Los Angeles
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $46–$63 | $95k–$132k |
| Mid level | $63–$88 | $132k–$183k |
| Senior | $85–$114 | $177k–$238k |
Adjusted for the Los Angeles 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
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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
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Data Engineer
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Senior Data Engineer
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Staff Data Engineer
05
Data Platform Lead
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