Data Analyst, Stewardship

DRW•Chicago, IL

7d•$100,000 - $120,000•Hybrid

About The Position

As a Data Analyst, Stewardship you will play an integral role in all operational aspects of onboarding, managing and maintaining various datasets used by traders and quantitative researchers. You will work closely with a variety of stakeholders, including technology, front office, operations, research, and risk management. You should be comfortable investigating complex data support issues in a fast-paced environment. This role is built around production support: Keeping EOD valuation data and PnL production running correctly, and being the person who catches, diagnoses, and fixes data problems before they flow to downstream applications. You'll work across a global support queue, troubleshoot financial instrument reference and valuation data, and partner with engineering to turn recurring fixes into lasting solutions.

Requirements

Proficiency in SQL and Python, with hands-on experience troubleshooting data issues or writing ad-hoc scripts to fix problems quickly under production pressure.
Familiarity with the Bloomberg Terminal and Bloomberg's Excel API plug-in is helpful; exposure to S3/Athena and Bloomberg back-office files is a plus
Comfortable with financial instruments and markets well enough to understand what the data actually represents.
Calm under pressure, with strong critical thinking — able to assess the real impact of a request or problem and respond with the right level of urgency.
Comfortable fielding requests from a global, technical user base across Slack and email, and explaining data behavior clearly to non-technical and technical audiences alike.
Experience building data governance rules/controls and writing runbook procedures for resolving support issues, so fixes are repeatable and not dependent on any one person's tribal knowledge.

Responsibilities

Monitor and support the production of EOD valuation data and PnL, including coverage during evening hours to bridge global support windows.
Respond to global requests via Slack and email to create, update, and troubleshoot financial instrument reference data.
Investigate and explain why and how data appears the way it does — tracing issues back through source systems, feeds, and business logic.
Diagnose data problems quickly under time pressure and build ad-hoc scripts or fixes to resolve them.
Assess the true downstream impact of a data issue or request before acting, prioritizing accordingly.
Work across reference data and valuation data stores and related reference data tooling.
Escalate and collaborate with engineering and other Data Management teams on systemic issues.
Identify recurring or repetitive data issues, and design and build new tooling to monitor for them before they impact users.
Partner with engineers to scope and deploy long-term, strategic fixes in the underlying code for problems that show up repeatedly, rather than resolving the same issue manually each time.