Roles and Responsibilities
Architect and build robust, scalable data pipelines to ingest and process billions of trade-level Present Value (PV) calculations.
Develop and optimize large-scale aggregation jobs using Apache Spark for maximum processing efficiency.
Load and model massive aggregated datasets into high-performance OLAP engines such as Apache Pinot, Apache Druid, and Trino.
Design and deliver intelligent data APIs for adaptable, on-demand access to aggregated and non-aggregated risk metrics.
Integrate Natural Language Processing (NLP) capabilities to create intuitive, query-based exploration interfaces.
Construct interactive analytical tools and dashboards to provide summary views and sub-second drill-down features.
Partner directly with senior stakeholders in Front Office, Quantitative teams, and Risk Management to deliver custom analytics.





Preferred Candidate Profile
Experience: 7 to 10 years of overall experience in big data engineering and complex system architecture.
Technical Expertise: Advanced proficiency in Java, Scala, or Python, expert SQL skills, and deep expertise with Apache Spark.
Database & Storage: Direct experience modeling and querying OLAP engines like Apache Pinot, Apache Druid, or Trino.
Domain Knowledge: Hands-on understanding of market risk, derivatives, VaR, Stress Testing, and FRTB regulations.
Assumed Requirements: Proficiency with Cloud platforms (AWS/GCP/Azure), CI/CD automation, Git, and Docker/Kubernetes containerization.

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