Job DescriptionRole Overview:
We are building a next-generation multi-asset trading and research infrastructure that ingests millions of ticks per second across global markets.
As Senior Data Associate, you will own the data foundation from acquisition to transformation ensuring that every research and trading system runs on clean, reliable, and latency-aware data.
You’ll work closely with our Quant, Research, and Execution teams to:
Design efficient data pipelines for market, fundamentals, and alternative datasets (Medium Frequency Trading + second-level data).
Automate ingestion, validation, and storage across multiple exchanges and brokers.
Build query able databases that power both live and back test environments.
This is an independent ownership role you’ll be expected to design, implement, and maintain without daily supervision.
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.
Benefits
- Stocked Snack Drawer: Enjoy a variety of snacks to keep you energised through the day
- Prime Location: Our office is well-connected and within walking distance from the railway station.
- Young, Driven Team: Collaborate with a sharp, motivated, and intellectually curious peer group.
- Clean, Well-Maintained Workspace: Simple, quiet, and conducive to focused, high-quality output.
RequirementsSkills & 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.