Senior Data Engineer - Philadelphia, 19109, United States of America How we LEAD: We are seeking an experienced and driven Senior Data Engineer Enterprise Data Products within the Global Data & Analytics team. You are pa…
Engineering & IT · Philadelphia, PA
Data Engineer jobs in Philadelphia, PA
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.
162
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$40–$101/hr
Typical pay range
$139k
Median, full-time
8
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01
Open data engineer roles
11 shown of 162 · sorted by freshness
JOB SUMMARY: The Senior Analytics Engineer is responsible for designing, building, and maintaining the enterprise data models that power reporting and analytics across C3 Industries and High Profile Cannabis Shops. Lever…
$71.3k - $140.6k
Position Summary Our Deloitte AI & Engineering team works to transform technology platforms, drive innovation, and help make a significant... ...fuel growth through innovation. Work you'll do As a PROJECT - Data Engineer…
technical stakeholders. The role supports product trial initiatives, release operations activities, and ongoing coordination across engineering and business teams. Responsibilities: Manage and track incoming work request…
Analyst to support and enhance a large-scale real-time reporting platform used by thousands of business users. This role blends data engineering, application support, and stakeholder engagement, making it ideal for someo…
processes that run it. That work changes the operating model an engineering organization runs on, the ways of working underneath it, and... ...We combine our strength in technology and leadership in cloud, data and AI wi…
Title: Data 360 Data Conversion Engineer Location: Philadelphia, PA USA (Onsite) Duration: 06+ Month contract role Position Overview We are seeking a Data Conversion Specialist to support leading data migration and conve…
Duties:Job Summary:Demonstrates basic knowledge of Tier 1 service level support as relates to addressing Hardware (PCor End-User Devices & peripheral equipment), and application software, and operating system issues.Demo…
We are seeking a Senior Data Engineer to design, develop, and maintain scalable data pipelines and data ingestion processes using modern Big Data technologies. The ideal candidate will have strong experience in data aggr…
development as required. Participate in technical design discussions, code reviews, and architecture decisions. Improve AI-assisted engineering workflows and development standards. Collaborate with architects, engineers,…
possible. To achieve this vision, Aramark will deliver strategic data assets that enable our operators to maximize the hospitality... ...for our consumers and clients. We are seeking a Junior Data Engineer who will work…
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What data engineers earn in Philadelphia
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $40–$56 | $84k–$117k |
| Mid level | $56–$78 | $117k–$162k |
| Senior | $75–$101 | $157k–$211k |
Adjusted for the Philadelphia 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
03
Senior Data Engineer
04
Staff Data Engineer
05
Data Platform Lead
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