Senior Java Spark Backend Engineer - Hybrid Contract

Motion Recruitment; Jersey City (NJ); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Motion Recruitment is assisting a global bank in Jersey City, NJ to hire a Java Spark Backend Developer for a hybrid contract role. You'll work onsite three days a week on a high-performance risk analytics platform and contribute to scalable data models and APIs.

The role emphasizes deep technical breadth in Spark, Java/Python, and risk-domain knowledge, with a focus on performance tuning and data fidelity for BI and AI use cases.

Qualifications

• 10+ years of hands-on, large-scale data engineering experience in finance.
• Rare blend of computer science depth and financial engineering focus.
• Deep knowledge of risk aggregation methods like VaR, CCAR, Stress Testing, FRTB.
• Strong performance metrics focus from CPU to network I/O.
• Proficient in Java and/or Python with expert SQL skills.

Responsibilities

• Architect and build a high-performance metadata-driven aggregation platform using Spark.
• Design algorithms to reduce high-dimensional risk aggregation complexity.
• Develop a rich semantic data model supporting BI and AI queries.
• Tackle Spark challenges, optimize partitioning and minimize shuffles for speed.
• Create a self-service platform enabling quants to define new aggregations.
• Build APIs and curated datasets for notebooks enabling analysis and replication.
• Load final metrics into OLAP stores like Pinot and Druid for analytics.

Skills

Java

Python

SQL

Data engineering

Performance tuning

Big data

Education

Degree in Computer Science or financial field

Tools

Apache Spark

Apache Pinot

Apache Druid

Job description

Motion Recruitment is assisting a global bank in Jersey City, NJ to hire a Java Spark Backend Developer for a hybrid contract role. You'll work onsite three days a week on a high-performance risk analytics platform and contribute to scalable data models and APIs.

The role emphasizes deep technical breadth in Spark, Java/Python, and risk-domain knowledge, with a focus on performance tuning and data fidelity for BI and AI use cases.