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. Originally posted on Himalayas