Senior Java/Spark Developer

Job Description

Job Description

Global Financial Firm located in Jersey City, NJ has an immediate contract opportunity for an experienced Senior Java/Spark Developer

Pay rate range: $ 91.00 - $ 96.00 Negotiable based upon years of experience
Work Mode: Hybrid
Location: Jersey City, NJ Responsibilities:

• Architect and build a high-performance, metadata-driven aggregation platform using Apache Spark.
• Design and implement algorithms to reduce the computational complexity of high-dimensional risk aggregations.
• Create a rich semantic layer over aggregated data, enabling both traditional BI and next-generation AI-driven queries.
• Solve computer science challenges within Spark, optimizing data partitioning strategies and minimizing network shuffling.
• Build a self-service platform empowering quants and risk managers to define new aggregations without writing new code.
• Develop high-performance APIs for UIs and provide curated datasets for Python Notebooks.
• Load final, aggregated risk metrics into high-performance OLAP stores like Apache Pinot and Apache Druid.

Requirements:

• Minimum 10 years of professional experience in large-scale data engineering, preferably within Capital Markets or Risk technology.
• Expert-level, hands-on experience with Apache Spark, including deep knowledge of its internal architecture and performance tuning.
• Experience designing and building semantic layers or data models for business intelligence/AI applications.
• Deep domain knowledge of risk aggregation methodologies for VaR, CCAR, Stress Testing, and FRTB.
• 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.

Preferred Skills:

• A rare blend of deep computer science knowledge and financial engineering expertise.
• An obsession with performance metrics, from CPU cycles and memory footprints to network I/O.
• A knack for solving complex data reconciliation and performance problems at scale.