Data Engineer, Java, Apache Spark, Hive - Assistant Vice President

Want to solve one of the most challenging big data problems in finance today? Are you excited by the idea of working directly with front-office and risk quants on massive-scale analytics for critical regulations like FRTB? Do you want to tame billions of rows of complex financial data with Apache Spark and deliver insights to senior leaders in a fraction of a second? Citi is looking for an elite, hands-on technologist to engineer the next-generation analytics platform for our Market Risk organization. This isn't just another data job. You will be the architect and builder of a high-performance system that ingests, processes, and serves petabytes of risk calculation data, making it instantly accessible and understandable to the entire firm. Your Role and Impact As a Senior Technologist, you will be at the heart of the action, designing the systems that power our most sophisticated risk-based calculations. You will take the raw, trade-level Present Value (PV) outputs from our Historical VaR and FRTB Expected Shortfall engines and transform them into a strategic data asset. Your impact will be immediate and far-reaching. You will build the data pipelines that handle immense volumes, create intelligent APIs that democratize data access, and leverage cutting-edge OLAP and NLP technologies to provide unparalleled drill-down and analytical capabilities. You will be the go-to expert who empowers senior stakeholders in the Markets and Risk organizations to make faster, smarter decisions. Key Responsibilities

Architect and build robust, scalable data pipelines to ingest and process billions of trade-level PV calculations from various stress engines.

Develop and optimize large-scale aggregation jobs using Apache Spark, ensuring high performance and efficiency.

Design and deliver a suite of "intelligent data APIs" that provide flexible, on-demand access to both aggregated and non-aggregated risk data for teams across the firm.

Integrate Natural Language Processing (NLP) capabilities to create intuitive, query-based interfaces for data exploration, lowering the barrier to entry for complex analytics.

Load and model massive aggregated datasets into high-performance OLAP engines like Apache Pinot, Apache Druid, and Trino.