We are looking for a Data Engineer to join a fast-paced IT consulting environment in Atlanta, Georgia. In this role, you will design and optimize modern data solutions that support analytics, machine learning, and busine…
Engineering & IT · Atlanta, GA
Data Engineer jobs in Atlanta, GA
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.
563
Open roles today
$38–$96/hr
Typical pay range
$132k
Median, full-time
8
Fresh in this list
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01
Open data engineer roles
9 shown of 563 · sorted by freshness
Inspire Brands is hiring a Lead Data and AI Engineer for the Enterprise Data Organization to design, build and manage data pipelines (Data ingestion, data transformation, data distribution, quality rules, data storage et…
$205.32k
Family GroupEngineering / Product DevelopmentJob ProfileSr Lead Data EngineerManagement LevelSr Manager - Non People LeaderFlexible Work... ..., LLCSr. Lead Data EngineerJob Description: Sr. Lead Data Engineer positions…
$40 - $45 per hour
Data Center Field Engineer Dell PowerEdge Servers (Travel Team)OverviewJoin a high-impact team supporting some of the most advanced AI and enterprise computing environments in North America. We are seeking experienced Da…
$202k - $229k
Job Description Summary: At The Coca-Cola Company, we believe data is the foundation for creating personalized experiences and powering... ...a visionary yet hands-on leader capable of advancing modern engineering practi…
Managing Director, Data Engineering & AIWho You'll Work WithAs a Managing Director in Slalom's Data & AI practice, you will lead the growth of our Data Engineering & AI business by helping clients modernize their data fo…
Job-ID28042269Reference26-10940 Modern Data Engineer (Enterprise + Lakehouse + AI‐Assisted) Experience 5+ years working with data in a software or data engineering role Experience in enterprise environment…
$67 per hour
Role : Senior Data Engineer( Medicaid or Healthcare Experience(Required)) Client: Government of Nebrasaka(NeDHHS) Work Type : Remote Duration : 12 Months Max Payrate : 67$ Per Hour OpenKyber is a fast-growing Systems Int…
We are seeking an experienced Technology Lead / Azure Data Engineer with strong hands-on expertise in Azure Synapse Analytics and PySpark. The ideal candidate will have a deep understanding of Apache Spark, distributed d…
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What data engineers earn in Atlanta
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$53 | $80k–$110k |
| Mid level | $53–$74 | $110k–$153k |
| Senior | $71–$96 | $148k–$199k |
Adjusted for the Atlanta 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
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Senior Data Engineer
04
Staff Data Engineer
05
Data Platform Lead
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