Market Data Team Lead

We are building a world-class data platform to support systematic trading across 10+ global exchanges. This role will lead the Data Engineering team responsible for ingesting, processing, storing, and serving all market, reference, and alternative data with low latency and high reliability. You will also own the architecture and evolution of our Feature Store, which powers our live trading systems and research pipeline.

This is a hands-on technical leadership position with significant ownership and influence over our trading technology stack.


Key Responsibilities
You’ll drive end-to-end delivery of key data engineering projects, such as:

• A unified platform for collecting and serving market data.

• Real-time and batch pipelines that transform raw data into usable features.

• A central store for offline, online, and streaming features.

• Frameworks to ensure data quality, consistency, and reliability.

• Tools for managing historical datasets and replaying data for research.

• Scalable storage and processing systems used by researchers and production systems.

Lead & Deliver

• Grow and mentor a Data Engineering team.

• Translate business and research needs into clear plans and roadmaps.

• Set engineering standards around code quality, testing, and CI/CD.

• Drive execution of the team’s long-term data platform strategy.

Own Data Engineering

• Oversee ingestion and processing of live and historical datasets.

• Define SLAs, data quality metrics, and monitoring.

• Manage schemas, storage formats, and dataset governance.

• Standardize data representations across different sources.

Feature Platform Ownership

• Design architectures for generating and serving features.

• Maintain low-latency pipelines used in production systems.

• Oversee intra-day feature computation and transformations.

• Ensure reproducibility, versioning, and lineage across the feature stack.

Infrastructure & Tooling

• Build and maintain data collectors and ingestion services.

• Manage scalable storage solutions for research and production use.

• Own metadata and cataloging tools.

• Develop compute workflows for feature updates and model support.

Collaboration

• Work closely with researchers to enable fast experimentation.

• Partner with engineering teams to ensure smooth integration into production systems.

• Collaborate with compliance and risk functions on governance and auditability.