Data Analytics Engineer

Sophisticated Work. In a Great City. Making a Difference. The State of Wisconsin Investment Board (SWIB) manages more than $178 billion in assets, including those of the fully-funded Wisconsin Retirement System (WRS). SWIB operates at a level more often seen in top-tier global asset managers than in typical public pension funds. SWIB is a home for top talent. Approximately 61 percent of SWIB's investment professionals are Chartered Financial Analyst (CFA) charter holders.

The City of Madison, the state capitol and home of Wisconsin’s flagship university, makes regular appearances on lists of best places to live, eat, and play. SWIB offers a modern workspace, hybrid work options, and competitive compensation and benefits. Serving over 703,000 WRS beneficiaries, SWIB is driven by a clear mission: securing the financial future of those who serve Wisconsin.

When you work at SWIB, you know your work matters.

SWIB does not sponsor employment-based visas for this position. Candidates must be authorized to work in the United States and be able to maintain that authorization throughout employment without requiring SWIB sponsorship.

The Data Delivery and Operations Division partners with Investment Management, Operations, Risk, and Technology to deliver trusted, timely, and analytics-ready data. The Data Analytics Engineering team governs investment and reference data within the analytics environment. The team transforms source data into reliable data assets that support reporting, performance measurement, risk analysis, accounting, trading, and other investment platforms.

Reporting to the Manager, Data Analytics Engineering, the Senior Data Analytics Engineer is a senior individual contributor responsible for investment data solutions across security master, entity master, reference data, pricing, holdings, and related domains. This role requires a strong understanding of how securities and other investment assets are identified, classified, priced, and mastered. The Senior Engineer traces information through connected systems, diagnoses why data did not flow or transform correctly, and coordinates durable solutions with business teams, engineers, Technology, and external data providers.

The role combines investment data expertise with analytics engineering and applied statistical methods. The Senior Engineer uses SQL and Python, works within Git-based development practices, and uses established CI/CD pipelines to test and promote changes. This person must be able to learn unfamiliar tools, understand how they fit into SWIB's data environment, and apply technology to a range of business and data problems.

The position also provides technical guidance and mentoring to other analysts but does not have direct staff-management responsibility.

Serve as a subject matter expert for security master, entity master, reference data, pricing, holdings, and related investment data. Interpret identifiers, classifications, instrument and issuer relationships, currencies, market conventions, corporate actions, price sources, valuation timing, and other attributes that affect investment processes. Define and maintain source-selection, golden-source, and match and master rules for assigned data domains.

Trace data from external providers through ingestion, mastering, transformation, validation, and downstream consumption. Investigate securities, prices, identifiers, classifications, holdings, and other records that are missing, stale, duplicated, incorrectly mapped, or rejected. Assess the business impact of data issues and coordinate resolution across Investment Management, Operations, Risk, ETL Engineering, Technology, and external providers. Participate as needed in after hours on call rotation in case of critical data delivery failures Identify recurring failure patterns and implement monitoring, validation, automation, or exception-handling improvements that reduce manual intervention.

Develop and optimize SQL and Python transformations, data models, reconciliations, validation routines, and analytics-ready datasets. Use Git for branching, commits, pull requests, code reviews, and merge conflict resolution. Use established CI/CD pipelines to execute tests, review results, promote approved changes, and validate deployments. Apply peer review, automated testing, controlled deployment, observability, and…