• Design, build, and optimize the data infrastructure that powers reporting, analytics, and business intelligence capabilities • Manage data synchronization across disparate database paradigms and enable stakeholders through modern BI tools • Safeguard core production databases from intensive analytical workloads and unauthorized data extraction • Write clean, maintainable, and typed code within the existing software ecosystem • Develop backend services, scripts, and internal tools leveraging TypeScript and the MERN stack

• Design, build and own the Bronze → Silver → Gold lakehouse architecture in Microsoft Fabric, following medallion design principles for progressive refinement of data. • Develop robust, idempotent data pipelines with data quality checks and validation at each tier boundary. • Define and maintain data products, data models, ownership boundaries, SLAs and documentation for the Data Hub platform. • Work with architects and stakeholders to define and follow data architecture standards across the platform. • Ensure data accuracy, completeness and consistency across data sources, transformation layers and consumer interfaces. • Identify, troubleshoot and resolve data‑related issues in pipelines, schemas, transformations and downstream consumption. • Collaborate closely with Data Scientists, Data Analysts and business units to understand data requirements and deliver fit‑for‑purpose data products. • Provide technical guidance and support on data‑related topics across teams. • Document data flows, schemas, transformations, pipelines and operational processes to support maintainability and team scalability.

Develop and maintain efficient data models and pipelines, oversee data quality and governance efforts, and collaborate with cross-functional teams to deliver scalable data solutions that support strategic decision-making and analytics.

• Capture & Ingestion. Own the full capture path from wire to lake: decode and normalize raw exchange feeds (pcap, multicast UDP / ITCH / FIX) and vendor sources (OneTick, Refinitiv, Bloomberg, ICE) into a unified canonical model with nanosecond timestamps. Build batch + stream pipelines (Airflow, Spark, dbt) for tick and reference data. Own L2/L3 order-book reconstruction with gap handling. Provide Python and Rust producer SDKs for internal feed handlers. • Storage & Modeling — Apache Iceberg. Own the Iceberg-over-S3 lakehouse: design partitioning, sort orders, and row-group layout for fast scans; manage schema evolution, snapshots, time travel, compaction, and TTL. Maintain reference data as slowly-changing tables with point-in-time correctness for backtests. Drive storage cost optimisation via compaction, tiering, and snapshot expiry. • Tooling & Libraries. Build libraries for schema management, data contracts, validation, and lineage on top of the Iceberg catalog. Develop shared access services (Spark + Polars) so Research, backtesting, and trading share one normalized data layer, including gap detection and pcap-vs-lake reconciliation. • Reliability & Observability. Embed monitoring, alerting, SLAs/SLOs, and CI/CD across capture and pipeline layers on Kubernetes (EKS). Own data-quality dashboards and incident runbooks for the capture fleet. • Collaboration. Partner with Quant Research, Data Science, Backend, and DevOps to translate requirements into platform capabilities and champion market-data engineering best practices.