Staff Engineer, Data Platform

Production e

About DRW

is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it.

is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

Unified Platform (UP)

ClickHouse, and the ability to guide platform us ers on the performance and operational trade-offs among them.

Responsibilities

• organization builds and offers managed data services, shared infrastructure, and metadata management systems that underpin our common query layer and power batch and streaming pipelines that power teams across DRW.

focused on the

• Data Platform, you will bring experience scaling data platforms to shape and uphold DRW’s long-term data platform vision, translating data strategy into engineering products and managed services used by Traders, Quantitative Researchers, and Back-Office personnel. You will work across tooling, producer, and consumer teams to connect business priorities with technical direction and hands-on delivery. This is an individual contributor role combining strategic thinking, architectural leadership, software development, and mentorship, with responsibility for technical initiatives that span teams.
• 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

• 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 b uild platform capabilities for metadata management, discovery, lineage, schema evolution, and data qua lity.
• Partner with security and data stakeholders on access controls and usage tra cking.
• 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.
• 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

• feedback into product and roadmap decisions.
• Experience scaling metadata management systems, such as data catalogs or lineage tracking systems
• that
• span
• multiple storage and
• Expertise
• in Java
• and/
• systems at scale
• practical knowledge of state management, replay, backpressure, and delivery guarantees.
• ge

Requirements

• 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

• 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.
• building
• both batch and stream
• demonstrating

deploying

• data processing and query technologies such as Flink, Spark, and
• Experience with multiple storage technologies, including object storage, relational or NoSQL databases, and event streaming systems such as Kafka
• In-depth k
• nowl

Extensive e

• xperience with data modeling, schema governance and evolution, and formats and serialization systems such as Arrow, Parquet, Protobuf, or Avro.
• building and operating cloud-native data infrastructure, including Kubernetes, workflow orchestration, automated deployment, and production monitoring.
• Excellent written and verbal communication skills, with the ability to communicate a technical vision, explain trade-offs, align stakeholders, and lead through influence across a globally distributed organization.
• Strong technical judgment and problem-solving skills, with the ability to prioritize competing needs, deliver incrementally in a fast-paced trading environment, and develop other engineers.

Preferred

• Experience scaling managed data services across teams with different workloads, service expectations, and operational constraints.

• of the internals of open table formats such as Iceberg or
• Delta Lake.