Data Analytics Engineering Manager

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) charterholders.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.

Job Description:

About the TeamThe Data Delivery and Operations Division partners across Investment Management, Operations, Risk, and Technology to deliver trusted, timely, and analytics-ready data that powers investment decisions.

The Data Analytics Engineering team serves as SWIB’s enterprise owner of investment and reference data within the analytics environment. The team transforms raw data into governed, analytics-ready assets that support reporting, performance measurement, risk analysis, accounting, and downstream investment platforms.

Position OverviewThe Manager, Data Analytics Engineering provides overall leadership and accountability for SWIB’s investment data and analytics engineering function.

This role owns the integrity, availability, and quality of SWIB’s investment data assets, including reference data, security master, and entity master domains. The Manager is accountable for data quality SLAs, daily data validation, and the reliability of analytics-layer data supporting investment, trading, risk, and portfolio management activities.

The Manager plays a critical role in overseeing the start-of-day data process to ensure investment teams have accurate and complete data to support daily trading and portfolio management decisions. This includes leading a 24x6 operational support model for data processing, validation, issue triage, and resolution.

This position also provides strategic oversight of key vendor relationships supporting investment and reference data, including SimCorp. The Manager ensures vendor performance aligns with SLAs, data quality expectations, and enterprise investment requirements.

The successful candidate combines strong investment data expertise, operational leadership capability, and proven people management experience. This role routinely applies significant judgment to moderate-to-complex business and data challenges and plays a central role in SWIB’s enterprise data governance and operational readiness.

Essential Activities

Investment Data Ownership & Master Data Management• Serve as enterprise owner of analytics-layer investment data, including reference data, security master, entity master, holdings, and related datasets.• Lead SWIB’s single source of truth strategy for security master and related master data domains.

• Establish governance standards, stewardship models, and documentation practices for master data assets.• Rationalize redundant or conflicting data sources to improve consistency across the organization.• Ensure alignment between master data architecture and investment, performance, risk, and accounting requirements.

Business Translation & Analytics Engineering• Partner closely with Investment Management, Operations, Risk, and ETL Engineering to translate business requirements into scalable analytics-ready data models.• Partner with ETL Engineering and Technology to ensure analytics transformation processes align with enterprise architecture and operational standards.• Provide architectural oversight for the analytics transformation layer to ensure data models, validation processes, and documentation practices meet enterprise standards.

Data Quality & Governance• Design and implement enterprise data quality frameworks within the analytics layer.• Own and report on data quality SLAs, including timeliness, completeness, and accuracy metrics.• Develop and publish recurring SLA and data quality reporting for leadership and stakeholders.• Ensure data validation controls are embedded in daily processing workflows.• Collaborate with upstream data providers and ETL Engineering to remediate systemic data issues.• Embed governance controls into analytics datasets to improve auditability andtransparency.

Vendor & Platform Relationship Management• Maintain strategic oversight of major vendor partners providing investment and reference data, including SimCorp.• Ensure vendor deliverables meet contractual SLAs and enterprise data qualityexpectations.• Lead regular governance meetings and performance reviews with SimCorp and other key vendors.• Partner with Technology and Investment Operations to address vendor-related data or platform issues.

Downstream Platform Support & Operational Readiness• Own the start-of-day data readiness process in support of daily trading and portfolio management activities.• Lead a 24x6 operational support model for data processing, validation, issue escalation, and resolution.

• Establish clear escalation procedures and incident response protocols for data-related production issues.• Ensure analytics-layer datasets support reliable exports to downstream systems, including Order Management Systems (OMS), risk analytics platforms, and other investment technologies.• Partner with Technology and ETL Engineering to maintain stable, governed export processes.

Cloud & Modernization Strategy• Co-design SWIB’s future-state cloud data architecture in partnership with ETL• Engineering and Technology.• Define clear architectural swim lanes between ingestion/orchestration (ETL• Engineering) and transformation/governance (Analytics Engineering).• Collaborate with ETL Engineering and Technology to support controlled deployment• processes and production governance standards.• Advance automation, reliability, and engineering discipline across analytics workflows.

Staff Leadership & Development• Lead, mentor, and develop a team of analytics engineering professionals.• Conduct annual performance reviews and provide ongoing coaching.• Elevate technical standards across the team in investment data modeling andgovernance practices.• Align hiring profiles with evolving operational and enterprise data needs.• Foster a culture of accountability, documentation, peer review, and continuousimprovement.

Qualifications Include• Bachelor’s degree in Data Analytics, Engineering, Information Systems, or related field; advanced degree or certifications preferred.• 8–12+ years of progressive experience in analytics engineering, data architecture, or investment data management.• Advanced SQL expertise and strong dimensional modeling experience.• Experience working with cloud-based data platforms (Azure, Snowflake, or comparable platforms).• Experience with master data management concepts and enterprise data governance.• Strong understanding of investment industry data, including security master, holdings, benchmarks, performance, and risk.• Demonstrated experience managing and developing technical staff.• Familiarity with modern production governance and controlled deployment practices preferred.

• 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