Staff Engineer, Data Platform

Description

The company is a diversified trading firm with over 30 years of experience. It trades with its own capital and takes its own risk, and it values autonomy and the ability to move quickly on opportunities.

The firm is headquartered in Chicago with offices across the U.S., Canada, Europe and Asia. It trades Fixed Income, ETFs, Equities, FX, Commodities and Energy in all major global markets, and it has also moved into real estate, venture capital and cryptoassets.

The Unified Platform (UP) organization builds managed data services, shared infrastructure and metadata management systems. These support a common query layer and batch and streaming pipelines used across the firm.

This is a Staff Engineer role focused on the Data Platform. You will help shape and maintain the long-term data platform vision and turn data strategy into engineering products and managed services for Traders, Quantitative Researchers and Back-Office staff. You will work with tooling, producer and consumer teams to link business priorities to technical direction and hands-on delivery. It is an individual contributor role that mixes strategy, architecture, software development and mentoring, and you will own technical initiatives that cross team boundaries.

Responsibilities

• Help define and maintain the Data Platform vision, guiding its architecture and how it evolves.
• Work with engineering leads, product managers and stakeholders to understand priorities, turn data strategy into products and platform features, and help shape the sequenced technical roadmap.
• Design, guide and build managed services for data storage, distributed query execution, batch and streaming processing and workload orchestration. Own each service from provisioning and upgrades through production support, and refine it using direct user feedback.
• Lead work across teams, turning unclear requirements into practical designs and measurable results while balancing urgent business needs against long-term platform goals.
• Run architecture reviews and technology decisions. Document trade-offs and build agreement on patterns that support the platform vision across the data ecosystem.
• Own the large-scale data governance strategy and build metadata management capabilities for discovery, lineage, schema evolution and data quality. Work with security and data stakeholders on access controls and usage tracking.
• Troubleshoot distributed systems, take part in production support and incident response, and drive lasting fixes through root-cause analysis, automation and operational documentation.
• Mentor engineers across teams through design reviews, code reviews and hands-on work, helping them build practices and take ownership of complex systems.

Requirements

• Extensive experience designing, building and operating large-scale, data-intensive distributed systems, including leading technical initiatives across multiple teams.
• Experience working with engineering leadership to speed up delivery across a team or organization while staying within existing processes.
• A track record of turning data or business strategy into engineering products and technical roadmaps, and balancing near-term delivery against a long-term platform vision.
• A track record of building managed platform services for other teams, owning provisioning, upgrades, reliability and support across the full lifecycle, and working directly with users to fold their requirements and feedback into product and roadmap decisions.
• Experience scaling metadata management systems, such as data catalogs or lineage tracking, that span multiple storage and compute technologies.
• Expertise in Java and/or Python, with a record of delivering high-quality, maintainable production software, and solid familiarity with SQL and Linux.
• Deep understanding of distributed systems trade-offs, including consistency, fault tolerance, concurrency, partitioning and recovery from partial failures.
• Production experience building batch and stream processing systems at scale, with 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 advise platform users on the performance and operational trade-offs between 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 serialization formats and 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, with the ability to convey 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 set priorities among competing needs, deliver incrementally in a fast-paced trading environment and develop other engineers.
• Desirable: experience scaling managed data services across teams with different workloads, service expectations and operational constraints.
• Desirable: hands-on knowledge of data governance in regulated environments.
• Desirable: familiarity with financial datasets and the needs of trading, quantitative research or back-office users.

Conditions

The annual base salary is $200,000 to $275,000, depending on experience, qualifications and relevant skills.

The company is an equal-opportunity employer.

Benefits

• The role is eligible for an annual discretionary bonus.
• Benefits include group medical, pharmacy, dental and vision insurance, a 401k with discretionary employer match, short and long-term disability, life and AD&D insurance, health savings accounts and flexible spending accounts.