Senior Investment Data Analyst, AI Enablement

***Please Note: This is a hybrid position requiring approximately 2-3 days per week onsite in the office, with the remaining time working remotely, consistent with team and business needs. ***

Get to Know the Opportunity

Voya Investment Management is seeking an experienced, analytical, and forward-thinking investment data professional to support the continued evolution of a trusted, scalable, and AI-enabled investment data environment.

This role serves as a critical bridge between investment business partners, data management, operations, technology teams, and external service providers. The successful candidate will combine investment-domain knowledge, business analysis skills, data analysis capabilities, and practical experience using modern AI-enabled tools to improve data quality, reporting, controls, and operational efficiency.

The ideal candidate understands how investment data moves across multiple systems and vendors, can translate stakeholder needs into actionable requirements, and is comfortable analyzing complex datasets to identify exceptions, patterns, and process improvement opportunities.

***Please Note: This is a hybrid position requiring approximately 2-3 days per week onsite in the office, with the remaining time working remotely, consistent with team and business needs. ***

The Contributions You’ll Make

Investment Data Analysis and Business Partnership

• Partner with investment, operations, technology, reporting, and data management teams to understand business objectives, data needs, and operational challenges.

• Gather, challenge, and clarify business requirements, including details stakeholders may not initially provide.

• Translate business needs into functional requirements, data specifications, acceptance criteria, testing scenarios, and process improvements.

• Analyze investment data across asset classes such as fixed income, equities, derivatives, and mortgage-related investments.

Investment Data Management and Data Flow Understanding

• Support integration, validation, and monitoring of data from internal systems, external investment platforms, custodians, administrators, and market-data providers.

• Develop a strong understanding of Voya Investment Management data models and the flow of data from source platforms through transformation processes into the investment reporting warehouse.

• Investigate data exceptions, identify root causes, and recommend enhancements to improve data quality, controls, and usability.

• Help maintain trusted, consistent, and business-ready investment data across downstream reporting and analytics environments.

Data Analytics and Technical Analysis

• Use SQL and Python to query, reconcile, validate, profile, and analyze large investment datasets.

• Assess current logic, transformation rules, reconciliations, and data feed requirements in partnership with technology teams.

• Support testing for new or changed data feeds, warehouse enhancements, reporting changes, and process improvements.

• Assist with analysis and validation in Snowflake or comparable data platforms, with focus on data extraction, interpretation, and business use rather than back-end platform administration.

AI Enablement and Process Improvement

• Use approved AI tools such as GitHub Copilot and Microsoft Copilot to improve requirements development, SQL and Python analysis, testing support, documentation, and exception analysis.

• Identify opportunities to simplify, standardize, automate, and reduce risk in recurring data processes and manual workflows.

• Validate AI-assisted outputs, protect confidential information, and maintain human accountability for analysis, decisions, and production changes.

• Support use cases related to anomaly detection, exception classification, root-cause analysis, data discovery, and workflow efficiency where appropriate.

Stakeholder and Vendor Collaboration

• Build trusted relationships with investment professionals, operations partners, technology teams, senior leaders, and external service providers.

• Communicate technical and data topics in clear business language, including scope, risks, dependencies, decisions, and delivery status.

• Support data interface work involving platforms and providers such as BlackRock Aladdin, Bloomberg, State Street, BNY, Charles River, and comparable systems.

• Collaborate with engineers and analysts to turn sound analysis and prototypes into secure, tested, documented, and production-ready solutions.

Leadership and Future Team Growth

• Operate as a senior individual contributor initially, with the potential to guide or develop team members as the group evolves.

• Share knowledge across investment data, requirements analysis, testing, exception management, stakeholder communication, and responsible AI practices.

• Contribute to a collaborative, accountable, and continuous-learning culture across business and technology partners.

Minimum Knowledge & Experience

• Bachelor’s degree in finance, accounting, economics, information systems, computer science, data analytics, engineering, or a related discipline. Equivalent relevant experience may be considered.

• Seven or more years of experience in investment management, asset management, investment operations, financial services, business analysis, data management, data analytics, or a related field.

• Strong knowledge of investment data and how it is used across front-, middle-, and back-office functions.

• Practical understanding of multiple asset classes, such as fixed income, equities, derivatives, structured products, investment funds, or comparable instruments.

• Significant experience with business analysis, process analysis, requirements definition, data mapping, data flows, data lineage, data controls, or data-quality management.

• Demonstrated ability to translate complex business needs into functional and technical requirements.

• Strong SQL skills, including the ability to query, join, reconcile, profile, and validate large datasets.

• Experience working with data warehouses, cloud data platforms, enterprise analytical environments, or large-scale investment data repositories.

• Experience planning and executing testing, including data validation, integration testing, parallel testing, regression testing, and user acceptance testing.

• Strong problem-solving skills and demonstrated ability to investigate data exceptions through root-cause analysis.

• Excellent written, verbal, facilitation, presentation, and stakeholder-management skills.

• Ability to manage changing priorities and strict deadlines, including month-end, quarter-end, and year-end processing periods.

• Demonstrated integrity, sound judgment, accountability, resilience, and commitment to high-quality execution.

Preferred Knowledge & Experience

• Experience with Snowflake or another modern cloud data platform, with emphasis on data extraction, analysis, validation, and interpretation.

• Working knowledge of Python for data analysis, automation, reconciliation, prototyping, or testing.

• Experience using Git-based source control and development workflows, preferably GitHub.

• Experience with approved AI-assisted development or analytical tools such as GitHub Copilot, Microsoft Copilot, Snowflake Cortex, or comparable enterprise AI tools.

• Familiarity with responsible-AI principles, including validation, human oversight, explainability, data protection, and monitoring of AI-assisted outputs.

• Experience with investment platforms or service providers such as BlackRock Aladdin, Bloomberg, State Street, BNY, Charles River, FactSet, SimCorp, or comparable systems.

• Experience building, supporting, or validating data interfaces to or from investment platforms, custodians, administrators, and market-data providers.

• Experience with market data, security master data, portfolio data, accounting data, performance, attribution, risk, regulatory reporting, or client reporting.

• Residential mortgage loan or mortgage investment data experience is helpful but not required.

• Exposure to APIs, JSON, Parquet, notebooks, Power BI, Streamlit, semantic models, data catalogs, metadata tools, or comparable analytics technologies.

• Familiarity with Agile delivery practices, including product backlogs, user stories, sprint planning, acceptance criteria, and retrospectives.

• Prior experience coaching analysts, leading workstreams, managing deliverables, or developing team members is beneficial.

• Professional designation or certification such as CFA, FRM, CPA, CBAP, product owner, cloud, data management, or Agile certification is a plus.