Senior Data Engineer- Financial Servies experience required

Liberty Personnel Services, Inc.; Philadelphia (Philadelphia County); On-site; USD 120,000 - 180,000; Full time

Job summary

Liberty Personnel Services, Inc. is seeking a Senior Data Engineer to build and own next-generation data platforms in financial services. Based in Philadelphia, you will transform massive operational data into trusted datasets for middle- and back-office teams.

You'll work with modern technologies like Spark, Databricks, Trino, Iceberg, Snowflake, Parquet, Kafka, and Python, designing scalable batch and streaming pipelines and delivering business-ready data products for operations and compliance.

Qualifications

• 5+ years in data engineering or large-scale data processing.
• Experience with batch and streaming data processing frameworks.
• Industry experience in capital markets or financial services required.

Responsibilities

• Design, develop, and support scalable data pipelines for middle- and back-office systems.
• Architect and implement modern data platform solutions.
• Build and optimize large-scale batch and streaming workflows.
• Collaborate with application teams to transform data to business requirements.
• Deliver business-ready data products to operations and compliance stakeholders.

Skills

5+ years in data engineering

Capital markets domain knowledge

Python programming

Data platform design

Distributed data processing

Performance optimization

Tools

Spark

Databricks

Trino

Iceberg

Snowflake

Parquet

Apache Arrow

Polars

Kafka

Job description

Senior Data Engineer- Financial Servies experience required

Senior Data Engineer - Build Next-Gen Data Platforms in Financial Services

Philadelphia, PA

Own the data engine behind critical financial operations

You’ll be the data engineer who turns massive, messy operational data into clean, trusted, business‑ready datasets that middle- and back-office teams can actually use.

This isn’t just “move data from A to B”. You’ll architect and build modern data platforms and pipelines that power trading operations, risk, reconciliations, and compliance across capital markets.

If you’ve built serious data systems in financial services and want to own the next generation of platforms, we want to talk to you.

What You'll Work On

You’ll sit on a Data Technologies team that partners directly with middle- and back-office application teams to:

• Next-generation data platforms for capital markets operations
• Scalable batch and streaming data pipelines
• Business-ready data products for Operations and Compliance
• Large-scale analytical datasets for middle- and back-office systems
• High-performance data processing and query environments
• Modern file and table formats for analytics at scale
• On-premises data platform components and integrations

You’ll work with modern data engineering technologies such as Spark, Databricks, Trino, Iceberg, Snowflake, Parquet, Apache Arrow, Polars, and Kafka, using Python as your primary development language.

Your core challenge: design and optimize data platforms that stay reliable and fast even as the data and the business keep scaling.

What You'll Be Doing

• Designing, developing, and supporting scalable data pipelines for middle- and back-office systems
• Architecting and implementing modern data platform solutions
• Building and optimizing large-scale batch data processing workflows
• Building and optimizing large-scale streaming data processing workflows
• Collaborating with application teams to model and transform data to meet business requirements
• Delivering business-ready data products to operations and compliance stakeholders
• Implementing and tuning high-volume data processing frameworks
• Optimizing queries and performance in large-scale analytical environments
• Monitoring data processing environments for reliability, scalability, and performance
• Troubleshooting and resolving issues across data pipelines and platforms
• Improving robustness and efficiency of existing data workflows
• Providing occasional off-hours or weekend support when needed

What We're Looking For

• 5+ years of experience in data engineering, data platforms, or large-scale distributed data processing
• Strong experience with modern data technologies such as Spark, Databricks, Trino, Iceberg, Snowflake, Parquet, Apache Arrow, Polars, and/or Kafka
• Hands‑on experience designing and optimizing high‑volume data pipelines
• Experience with large-scale batch and streaming data processing frameworks
• Proven background in query optimization and performance tuning in analytical environments
• Experience with on‑premises data platforms (preferred)
• Industry experience within capital markets or financial services (required)
• Domain knowledge in Operations and/or Compliance within financial services (highly preferred)
• Ability and flexibility to provide occasional off-hours/weekend support

The Experience That Will Really Get Our Attention

You’ve been the go-to engineer when data problems get hard: distributed joins blowing up, streaming jobs lagging, analytical queries timing out, and stakeholders who still need answers on time. You know how to design data models and pipelines that work in the real world of capital markets and post‑trade operations.

If you’ve paired strong Python engineering with hands‑on work in Spark- or Databricks-based platforms, dealt with on‑prem constraints, and delivered high‑performance datasets for operations or compliance teams, you’ll stand out here.

Why This Opportunity?

You’ll work close to the money, on the data that actually runs the business—operations, settlements, reconciliations, compliance—not vanity dashboards.

You’ll own substantial pieces of the data platform, from architecture through production support, with direct partnerships across middle- and back-office teams.

You’ll get to sharpen your skills on modern data technologies at serious scale, in a capital markets environment where performance, correctness, and reliability all matter.

Industry experience within capital markets or financial services is required.

Ability and flexibility to provide occasional off-hours/weekend support is required.

Interested?

If you’re a senior data engineer who thrives on large-scale, high-impact financial data challenges, let’s talk.

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