Key Responsibilities :

- Architect and maintain structured databases for tick-level and MFT data.

- Develop efficient ingestion & ETL pipelines using Python/C++, handling large volumes in near-real time.

- Write optimised SQL queries and manage schemas for time-series and reference data.

- Implement automated data validation, reconciliation, and version control.

- Work with quants to expose clean datasets for research and live trading systems.

- Ensure data integrity across time zones, instruments, and asset classes.

- Manage historical data archives and set up policies for retention, compression, and retrieval.

- Collaborate with the execution team to align live feeds with stored data.

Requirements :

Skills & Qualifications :

- Strong command over Python (pandas, multiprocessing, asyncio) and SQL (Postgres/MySQL).

- Working knowledge of C++ for performance-critical modules or parsers.

- Comfort with Linux environments, shell scripting, and version control (Git).

- Experience handling large-scale time-series data.

- Understanding of data normalization, schema design, and storage optimization.

- Ability to work independently, manage priorities, and deliver with accountability.

- Exposure to financial or tick-data pipelines, FIX/FAST feeds, or exchange APIs.

- Familiarity with Redis, Kafka.

- Prior experience in an HFT, quant, or data-heavy product firm