Senior Hands-on Technologist, Massive Scale Risk Analytics - Vice President

The Opportunity: Are You Ready to Build the Future of Risk Analytics?

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.
• Build powerful, interactive analytical tools and dashboards on top of the OLAP layer, providing summary views and lightning-fast drill-down capabilities.
• Partner directly with senior stakeholders in the Front Office, Quantitative teams, and Risk Management to understand their analytical needs and deliver innovative solutions.

What We're Looking For

• A true passion for data, analytics, and solving complex problems at massive scale.
• A degree in a quantitative or technical field such as Computer Science, Financial Mathematics, or Financial Engineering.
• Expert-level, hands-on experience with big data technologies, particularly Apache Spark.
• Proven experience with high-performance OLAP databases such as Apache Pinot, Apache Druid, or Trino.
• Strong programming skills in Python, Java and/or Scala, and expert-level SQL.
• Strong background in fundamental computer science concepts, including data structures and algorithms.
• A mindset for 'AI-first' development, constantly looking for ways to embed intelligence into systems.
• Experience or a strong interest in applying Natural Language Processing (NLP) to data access and analytics.
• A knack for solving 'needle in a haystack' problems, with a talent for debugging complex data and access issues.
• Experience in the financial industry with an understanding of market risk, derivatives, and risk calculations (VaR, Stress Testing, PV) is highly desirable.
• Exceptional problem-solving skills and the ability to work independently and lead technical projects.
• Excellent communication skills, with the confidence to collaborate with senior business and quantitative stakeholders.

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