Java Spark Backend Developer

Grow your career as a Java Spark Backend Developer with an innovative global bank in Jersey City, NJ. Contract role with strong possibility of extension. Will require working a hybrid schedule 3 days onsite per week.

Join one of the world's most renowned global banks and trusted brand with over 200 years of continuously evolving financial services worldwide. You will work alongside some of the smartest minds in the industry who are excited to share their knowledge and to learn from you.

Contract Duration: 5+ Months

Required Skills & Experience

• 10+ years of professional experience in hands-on, large-scale data engineering, with a significant portion within the Capital Markets or Risk technology space.
• You are a rare blend of a deep computer scientist and a savvy financial engineer, with a passion for solving problems from first principles.
• Expert-level, hands-on experience with Apache Spark, including deep knowledge of its internal architecture, performance tuning, shuffle optimization, and partitioning strategies.
• Experience designing and building semantic layers or data models that bridge the gap between complex physical data and business intelligence/AI applications.
• Deep domain knowledge of risk aggregation methodologies for VaR, CCAR, Stress Testing, and FRTB is essential.
• An obsession with performance metrics, from CPU cycles and memory footprints to network I/O.
• Strong programming skills in Java and/or Python, and expert-level SQL.
• Proven experience with high-performance OLAP databases such as Apache Pinot or Apache Druid.
• A degree in a quantitative or technical field such as Computer Science, Financial Mathematics, or Financial Engineering.
• A knack for solving complex data reconciliation and performance problems at scale.

What You Will Be Doing

• Architect and build a high-performance, metadata-driven aggregation platform using Apache Spark.
• Design and implement novel algorithms to reduce the computational complexity of high-dimensional risk aggregations.
• Design and build a rich semantic layer over the aggregated data, creating a business-friendly data model that can power both traditional BI and next-generation AI-driven queries.
• Solve deep computer science challenges within Spark, choosing the right underlying mechanisms, designing optimal data partitioning strategies, and minimizing network shuffling to ensure extreme performance.
• Build a self-service platform that empowers quants and risk managers to define new aggregations without writing new code.
• Pioneer innovative ways of making data available to users by building high-performance APIs for UIs, and providing curated datasets for Python Notebooks that enable deep forensic analysis and calculation replication.
• Load the final, aggregated risk metrics into high-performance OLAP stores (Apache Pinot, Apache Druid) to serve the analytics and UI layers.