Data Analytics Engineer

State of Wisconsin Investment Board • Madison, WI

9d • Hybrid

About The Position

The State of Wisconsin Investment Board (SWIB) manages over $178 billion in assets and is seeking a Data Analytics Engineer. SWIB operates at a high level, comparable to top-tier global asset managers, and is committed to attracting top talent. The City of Madison, where SWIB is located, is recognized as a desirable place to live. SWIB provides a modern workspace, hybrid work options, and competitive compensation and benefits. The organization's mission is to secure the financial future of WRS beneficiaries, ensuring that employees' work has a tangible impact. The Data Delivery and Operations Division, specifically the Data Analytics Engineering team, is responsible for governing investment and reference data within the analytics environment. This team transforms source data into reliable data assets crucial for reporting, performance measurement, risk analysis, accounting, trading, and other investment platforms. The Senior Data Analytics Engineer will report to the Manager, Data Analytics Engineering, and will be a key individual contributor focused on investment data solutions across various domains including security master, entity master, reference data, pricing, and holdings. This role demands a deep understanding of how investment assets are identified, classified, priced, and mastered. The engineer will be responsible for tracing data through connected systems, diagnosing data flow and transformation issues, and collaborating with business teams, engineers, Technology, and external data providers to implement lasting solutions. The position blends expertise in investment data with analytics engineering and applied statistical methods. The role utilizes SQL and Python, adheres to Git-based development practices, and leverages CI/CD pipelines for testing and deployment. The ideal candidate will be adept at learning new tools, understanding their integration into SWIB's data ecosystem, and applying technology to solve business and data challenges. While providing technical guidance and mentoring to other analysts, this role does not involve direct staff management.

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.
• The position requires U.S. work authorization.

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
• Hybrid work environment
• Relocation reimbursement to the Dane County area

What This Job Offers

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