Staff Engineer, Data Platform

What You'll Do: Contribute to and uphold DRW's overall Data Platform vision, guiding its architecture and evolution to advance the firm's data strategy Translate data strategy into engineering products and platform capabilities in collaboration with engineering leads, product managers, and stakeholders Design, guide, and build managed platform services for data storage, distributed query execution, batch and streaming processing, and workload orchestration, owning their full lifecycle Lead delivery across team boundaries by turning ambiguous requirements into practical designs and measurable outcomes while balancing immediate business needs with long-term platform goals Drive architecture reviews and technology decisions, building consensus around patterns that uphold the platform vision across the data ecosystem Own the strategy for large-scale data governance and build platform capabilities for metadata management, discovery, lineage, schema evolution, and data quality Troubleshoot distributed systems, contribute to production support and incident response, and drive lasting improvements through root-cause analysis and automation Mentor engineers across teams through design reviews, code reviews, and hands-on collaboration What You Need Extensive experience designing, building, and operating large-scale, data-intensive distributed systems, including technical leadership of initiatives spanning multiple teams Track record of translating data or business strategy into engineering products and technical roadmaps, with judgment to align near-term delivery with long-term platform vision Experience building and offering managed platform services to other teams, owning provisioning, upgrades, reliability, and ongoing support throughout the service lifecycle Experience scaling metadata management systems such as data catalogs or lineage tracking systems that span multiple storage and compute technologies Expertise in Java and/or Python with proven ability to deliver high-quality, maintainable production software; strong familiarity with SQL and Linux environments Deep understanding of distributed systems trade-offs including consistency, fault tolerance, concurrency, partitioning, and recovery from partial failures Production experience building both batch and stream processing systems at scale, demonstrating practical knowledge of state management, replay, backpressure, and delivery guarantees Experience deploying data processing and query technologies such as Flink, Spark, and ClickHouse, and ability to guide platform users on performance and operational trade-offs Experience with multiple storage technologies including object storage, relational or NoSQL databases, and event streaming systems such as Kafka; in-depth knowledge of open table formats such as Iceberg or Delta Lake Extensive experience with data modeling, schema governance and evolution, and formats and serialization systems such as Arrow, Parquet, Protobuf, or Avro Familiarity with building and operating cloud-native data infrastructure including Kubernetes, workflow orchestration, automated deployment, and production monitoring Excellent written and verbal communication skills and strong technical judgment with ability to prioritize competing needs and develop other engineers

Nice to Have: Experience scaling managed data services across teams with different workloads, service expectations, and operational constraints Hands-on knowledge of data governance in regulated environments Familiarity with financial datasets and the needs of trading, quantitative research, or back-office users Annual base salary $200,000-$275,000 depending on experience, qualifications, and relevant skill set; eligible for annual discretionary bonus. Comprehensive employee benefits including group medical, pharmacy, dental and vision insurance, 401(k) with discretionary employer match, short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts.