Drweng Staff Engineer at DRW focusing on scaling and evolving the Data Platform to support trading and research teams. Responsible for designing and building managed platform services and leading cross-team technical initiatives.
Responsibilities
Contribute to and uphold the overall vision for DRW’s Data Platform, guiding its architecture and evolution to advance the firm’s data strategy.
Work with engineering leads, product managers, and stakeholders to understand business priorities and translate data strategy into engineering products, platform capabilities, and contribute to the sequenced technical roadmap.
Design, guide, and build managed platform services for data storage, distributed query execution, batch and streaming processing, and workload orchestration, owning their lifecycle from provisioning and upgrades through production support, refining the managed offers from direct user feedback.
Lead delivery across team boundaries, 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, documenting trade-offs and 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. Partner with security and data stakeholders on access controls and usage tracking.
Troubleshoot distributed systems, contribute to production support and incident response, and drive lasting improvements through root-cause analysis, automation, and operational documentation.
Mentor engineers across teams through design reviews, code reviews, and hands-on collaboration, strengthening engineering practices and helping others take ownership of complex systems.
Qualification
Extensive experience designingExpertise in Java and/or PythonExtensive experience with data modeling
Required
Extensive experience designing, building, and operating large-scale, data-intensive distributed systems, including technical leadership of initiatives spanning multiple teams.
Experience coordinating with engineering leadership to accelerate team- and organization-wide delivery within existing processes and practices.
A track record of translating data or business strategy into engineering products and technical roadmaps, with the judgment to align near-term delivery with a long-term platform vision.
A track record of building and offering managed platform services to other teams, owning provisioning, upgrades, reliability, and ongoing support throughout the service lifecycle, working directly with users to incorporate their requirements and feedback into product and roadmap decisions.
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 a proven ability to deliver high-quality, maintainable production software, and 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 the ability to guide platform users on the performance and operational trade-offs among them.
Experience with multiple storage technologies, including object storage, relational or NoSQL databases, and event streaming systems such as Kafka. In-depth knowledge of the internals 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.
