Senior Java/Spark Developer

Job DescriptionJob DescriptionGlobal 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, NJResponsibilities: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.