Senior Java/Spark Developer

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.