Data Analytics Engineer

Join a mission-driven team at the State of Wisconsin Investment Board and help deliver trusted, timely, analytics-ready investment data. In this hybrid role in Madison, WI, you will take ownership of investment data solutions across core domains like security mastering, entity mastering, reference data, pricing, holdings, and related attributes. You will work in a professional environment with educational support, tuition reimbursement, and a benefits package aligned to AON (formerly McLagan) industry benchmarks.

What you’ll do

• Serve as a subject matter expert across security master, entity master, reference data, pricing, and holdings.
• Interpret identifiers, classifications, issuer and instrument relationships, currencies, market conventions, corporate actions, price sources, and valuation timing to ensure downstream investment processes are supported with correct attributes.
• Define and maintain source-selection, golden-source, and match and master rules for your assigned domains.
• Trace data end to end from external providers through ingestion, mastering, transformation, validation, and downstream consumption.
• Investigate records that are missing, stale, duplicated, incorrectly mapped, or rejected, including securities, prices, identifiers, classifications, and holdings.
• Assess business impact and coordinate resolution with teams including Investment Management, Operations, Risk, ETL Engineering, Technology, and external providers.
• Participate in an after-hours on call rotation when critical data delivery failures occur.
• Identify recurring failure patterns and implement monitoring, validation, automation, or exception-handling improvements to reduce manual effort.
• Develop and optimize SQL and Python transformations, data models, reconciliations, validation routines, and analytics-ready datasets.
• Use Git (branching, commits, pull requests, code review, and merge conflict resolution) and established CI/CD pipelines to test, promote, and validate deployments.
• Apply peer review, automated testing, controlled deployment, observability, and documentation practices to analytics workflows.
• Evaluate technologies and new AI capabilities based on the problem, and keep learning as the data environment evolves.
• Analyze historical patterns, distributions, relationships, and time-series behavior in investment and reference data.
• Design rule-based and statistical monitors for missing, stale, unusual, or inconsistent securities, prices, holdings, classifications, and other data, including thresholds and tolerances shaped by asset-class characteristics and normal variation.
• Back-test proposed controls and monitors against historical data, then refine logic based on false positives, false negatives, detection rates, and exception volumes.
• Evaluate advanced statistical or machine-learning techniques when they provide measurable advantages over deterministic rules.
• Explain statistical findings and automated alerts in practical business terms to keep results understandable and auditable.
• Implement preventive and detective controls for timeliness, completeness, accuracy, validity, consistency, uniqueness, and referential integrity, and monitor data-quality measures through investigation and remediation coordination.
• Lead complex initiatives by translating investment and operational needs into data models, transformation rules, validation requirements, test plans, and technical specifications.
• Review solution designs, data models, SQL, Python, test plans, and documentation, providing clear and actionable feedback.
• Mentor engineers in investment data, security mastering, pricing, statistical monitoring, troubleshooting, and engineering practices.
• Lead discussions with stakeholders across multiple business functions.

What you bring

• Bachelor’s degree in data analytics, data science, engineering, information systems, finance, or a related field.
• 6+ years of progressive experience in analytics engineering, investment data management, data architecture, securities operations, or a related discipline.
• Strong understanding of security and entity mastering, investment reference data, pricing, and how these data affect downstream investment processes.
• Advanced SQL skills and working proficiency in Python.
• Hands-on Git experience, including branches, commits, pull requests, code reviews, and merge conflict resolution.
• Experience with agile workflow tools such as Jira.
• Experience using established CI/CD pipelines to test, promote, deploy, and validate changes (design or administration of CI/CD infrastructure is not required).
• Experience implementing data-quality controls, reconciliations, exception workflows, root-cause analysis, lineage, and governance practices.
• Experience with cloud data platforms such as Snowflake, Microsoft Azure, or comparable technologies.
• Ability to learn unfamiliar tools, select technology based on the problem, lead cross-functional work, and communicate with both technical and investment audiences.

Technologies you’ll work with

• SQL, Python, Git, CI/CD pipelines, Jira, Snowflake, Microsoft Azure, AI

Benefits

• Competitive total cash compensation, based on AON (formerly McLagan) industry benchmarks
• Comprehensive benefits package
• Educational and training opportunities
• Tuition reimbursement
• Challenging work in a professional environment
• Hybrid work environment
• Weekly presence in offices (frequency dependent on distance from office or the job position)
• Relocation reimbursement to the Dane County area per company policy

About the team

The Data Delivery and Operations Division partners with Investment Management, Operations, Risk, and Technology to deliver trusted, timely, and analytics-ready data.

Preferred qualifications

• Master’s degree in data science, statistics, financial mathematics, computer science, or another quantitative discipline.
• Experience applying statistical analysis to data-quality or operational problems, including data profiling, distribution analysis, threshold design, time-series analysis, outlier detection, or anomaly detection.
• Experience working with multiple asset classes and their reference-data and pricing conventions.
• Experience with investment platforms or data providers such as SimCorp, Markit EDM, FactSet, Bloomberg, BlackRock Aladdin, MSCI, or Charles River Development.

Location: Madison, WI (hybrid)

Experience: 6+ years

Education: Bachelor’s degree