Data Analytics Engineer

State of Wisconsin Investment Board • Madison, WI

18d • Hybrid

About The Position

The State of Wisconsin Investment Board (SWIB) manages over $178 billion in assets and is seeking a Senior Data Analytics Engineer. This role is a senior individual contributor responsible for investment data solutions across security master, entity master, reference data, pricing, holdings, and related domains. The position requires a strong understanding of how securities and other investment assets are identified, classified, priced, and mastered. The Senior Engineer will trace information through connected systems, diagnose data flow and transformation issues, and coordinate solutions with business teams, engineers, Technology, and external data providers. The role combines investment data expertise with analytics engineering and applied statistical methods, utilizing SQL and Python, Git-based development practices, and CI/CD pipelines. The position also provides technical guidance and mentoring to other analysts but does not have direct staff-management responsibility. SWIB offers a modern workspace, hybrid work options, and competitive compensation and benefits. Candidates must be authorized to work in the United States and be able to maintain that authorization without requiring SWIB sponsorship.

Requirements

• 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 methodology workflow tools (Jira).
• Experience using established CI/CD pipelines to test, promote, deploy, and validate changes.
• 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 technical and investment audiences.
• Candidates must be authorized to work in the United States and be able to maintain that authorization throughout employment without requiring SWIB sponsorship.

Nice To Haves

• 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.

Responsibilities

• 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 documentation practices to analytics workflows.
• Evaluate technologies and new AI capabilities based on the problem being solved and learn new tools as SWIB’s 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.
• Establish thresholds and tolerances that reflect asset-class characteristics, market conditions, source behavior, and normal variation.
• Back-test proposed controls and monitors against historical data before implementation.
• Evaluate false positives, false negatives, detection rates, and exception volumes and adjust monitoring logic to improve its operational usefulness.
• Evaluate advanced statistical or machine-learning techniques when they provide a measurable advantage over deterministic rules.
• Explain statistical findings and automated alerts in practical business terms so that results remain understandable and auditable.
• Implement preventive and detective controls for timeliness, completeness, accuracy, validity, consistency, uniqueness, and referential integrity.
• Monitor data-quality measures, investigate exceptions, perform impact analysis, and coordinate remediation.
• Lead complex initiatives and translate investment and operational needs into data models, transformation rules, validation requirements, test plans, and technical specifications.
• Identify gaps in data architecture, controls, integration patterns, and support processes and recommend practical solutions.
• Review solution designs, data models, SQL, Python, test plans, and documentation and provide clear, actionable feedback.
• Mentor engineers in investment data, security mastering, pricing, statistical monitoring, troubleshooting, and engineering practices.
• Lead discussions with key stakeholders across multiple business functions.

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
• Relocation reimbursement to the Dane County area

What This Job Offers

• Job Type: Full-time
• Career Level: Senior