Data Analyst

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

The Data Analyst – Business Intelligence role will join DRW’s Unified Platform (UP) Data Management team. UP systems are responsible for data that spans the trade lifecycle: from research, execution, through to post-trade. Our business operations generate substantial data each day. Our global footprint produces inquiries from stakeholders that require complex answers. Questions can span years of data, multiple data stores and a variety of financial domains.

This is a reporting and analysis role at the intersection of finance, trading operations, and data engineering. The work is deadline-driven; your outputs go to regulatory authorities, external auditors, and firm leadership.

What You'll Do

Answer business questions with data

• Build and run extracts against the firm's data stores to answer requests from the front office, and other units like Tax and Legal
• Drive improvements to market share reporting and analytics
• Support quarterly and annual tax and regulatory inquiries across multiple global jurisdictions
• Respond to audit, litigation, and class-action data requests, often under short turnaround

Make the numbers trustworthy

• Validate every extract before delivery — cross-foot detail against aggregates, reconcile against source systems, and investigate discrepancies rather than explaining them away
• Document the business rules behind each report, including who made the decision and why

Reduce the recurring load

• Identify patterns across incoming requests
• Partner with engineering teams to translate those patterns into durable solutions: reporting dashboards, self-service views, scheduled reports
• Write the specification: define the business logic, the edge cases, and the validation criteria that a tool needs to get right