Lead Engineer Data Platform

Global proprietary trading firm with over a decade of success across equity derivatives, delta one, ETFs, commodity derivatives, and crypto. Technology drives our trading strategies, and we are building a brand‑new, next‑generation data platform to power both quantitative research and live trading. This is a high‑impact, long‑term role – not incremental fixes to legacy systems.

What You Will Do

• Ensure reliability, uptime, and effective capacity planning for all data workloads.
• Drive performance optimizations across both hardware and software for research and regulatory data retention.
• Design and implement a centralized data framework and the supporting applications.
• Manage, maintain, and expand dedicated hardware infrastructure for data systems.
• Partner with researchers, traders, and engineers to align data workflows with business needs.
• Build and lead a high‑performance engineering team, including mentoring and career development.
• Define and deliver the data platform roadmap, balancing business and technical priorities.
• Develop robust, maintainable, and high‑performing data pipelines.
• Standardize ingestion, schema management, lineage, permissions, and audit processes.
• Investigate upstream data sources, identify root causes of data issues, and implement sustainable fixes.

What You Offer

• 10+ years of experience delivering production data platforms, with 2+ years leading and mentoring engineering teams.
• Prior experience in quant trading, proprietary trading, or a buy‑side financial firm – this is essential.
• Proven ability to design, scale, and optimize data pipelines and distributed systems in production.
• Deep data platform fundamentals: batch and streaming processing, scalable table/metadata formats, object storage, and SQL.
• Hands‑on experience building scalable ingestion and orchestration (scheduling, dependency management, backfills, recovery).
• Experience with modern data architecture patterns and tradeoffs across performance, reliability, and cost.
• Solid understanding of data storage and file/table lifecycle management.
• Ability to make architectural decisions across IT solutions, system design, and implementation – ensuring maintainability and extensibility.
• Demonstrated track record of improving reliability and operational excellence for data systems.
• Excellent stakeholder communication skills with strong roadmap ownership.
• Ability to drive large‑scale change while iterating safely (rollouts, learning from outcomes, adjusting direction).
• Strong problem‑solving mindset with high attention to detail, including root‑cause analysis.
• University degree in Computer Science or a related discipline.
• Fluency in written and spoken English.