$165k - $180k
Data Engineering Manager Location: Remote (NY/NJ residents only) Direct Hire Overview Seeking a hands-on engineering leader to guide a small data engineering team while driving modernization of a cloud-based data platfor…
Engineering & IT · New York, NY
Data Engineer jobs in New York, NY
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.
1,790
Open roles today
$50–$124/hr
Typical pay range
$170k
Median, full-time
3
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01
Open data engineer roles
4 shown of 1,790 · sorted by freshness
$62 - $65 per hour
A client of OpenKyber is immediately hiring for the Senior ETL Focused Data Engineer Position Type: Full time, Hybrid Onsite Contract --- ( Hybrid Position 3 days onsite and 2 days remote work in a week!!!) Duration: 12-…
Role: Lead Data Engineer Location: North Carolina (Charlotte/Raleigh) Experience: 8+ Years Day 1 onsite Job Description: 8+ years of Technology Delivery / Program Management / Technical Project Leadership experience 5+ y…
$215.2k - $245.6k
Overview Lead Data Engineer Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Ca…
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What data engineers earn in New York
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $50–$69 | $103k–$143k |
| Mid level | $69–$95 | $143k–$198k |
| Senior | $92–$124 | $191k–$257k |
Adjusted for the New York 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.
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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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