Data Governance Lead

Over the last 20 years, Ares’ success has been driven by our people and our culture. Today, our team is guided by our core values – Collaborative, Responsible, Entrepreneurial, Self-Aware, Trustworthy – and our purpose to be a catalyst for shared prosperity and a better future. Through our recruitment, career development and employee-focused programming, we are committed to fostering a welcoming and inclusive work environment where high-performance talent of diverse backgrounds, experiences, and perspectives can build careers within this exciting and growing industry. Job Description Ares is seeking a Vice President of Data Governance to own and operationalize data governance across the firm. This person will build the frameworks, policies, and operating processes that make data trustworthy, discoverable, secure, and AI-ready — directly enabling the firm's Enterprise Data Strategy and the AURA AI agent platform. The role sits within the Data & AI team and works across all investment and corporate functions. This is a hands-on, build-from-the-ground-up mandate: the VP will design governance frameworks, stand up the tooling and processes to enforce them, and partner with Legal, Compliance, Risk, Cyber, and business stakeholders to ensure governance keeps pace with an aggressively expanding Data & AI ecosystem. Key Responsibilities Data Governance Framework & Strategy Design, implement, and continuously mature the firm's end-to-end data governance framework, including policies, standards, operating models, and RACI structures for data ownership and stewardship Define and drive the firm's data classification scheme (e.g., public, internal, confidential, restricted/PII) and ensure consistent application across Databricks/Unity Catalog and downstream systems Establish data governance councils/forums and stewardship roles across investment and corporate functions Align governance roadmap with the Enterprise Data Strategy and the AURA platform roadmap Data Catalog Management Own the data catalog strategy and administration (Unity Catalog), including taxonomy, metadata standards, business glossary, lineage tracking, and ownership tagging Ensure all critical data assets and supporting documents — fund master data, deal data, portfolio data, LP data — are cataloged, documented, and discoverable Partner with data engineering to embed catalog registration into the medallion architecture (bronze/silver/gold) as a standard part of the data lifecycle Partner with Compliance to ensure data meet retention requirements and manage the end-to-end data lifecycle. Data Quality Establish an enterprise Data Quality Framework Define Critical Data Elements (CDEs), data quality standards, metrics, and SLAs for critical data domains (fund, deal, portfolio, investor) Build data quality monitoring, issue management, and remediation workflows Develop executive dashboards and governance reporting on quality performance and trends and report quality KPIs to senior stakeholders Establish root-cause and continuous improvement processes for recurring data quality issues Access Management & PII Define and enforce data access management requirements, including role-based access control, least-privilege principles, and periodic access recertification Partner closely with IAM in Cyber to align data access management requirements with the firm's broader identity and access management framework, including provisioning, entitlement reviews, and control testing Own the firm's approach to identifying, classifying, and protecting PII and other sensitive data (e.g., investor data, employee data), including masking, tokenization, and retention requirements Partner with Cyber and Legal on data privacy compliance (e.g., state privacy laws, GDPR where applicable) and incident response for data exposure events Data Governance for AI Define governance requirements specific to AI/agentic systems built on AURA, including: Model & data lineage for AI: tracking which datasets, embeddings, and vector indices feed which agents, and ensuring traceability from source data to AI-generated output AI data access controls: governing what data each agent/persona is permitted to retrieve or act on via Unity AI Gateway, including guardrails against unauthorized cross-domain data access PII handling in AI pipelines: ensuring RAG pipelines, embeddings, and LLM prompts do not leak PII or confidential deal information; defining redaction/masking standards for data entering vector stores Model risk & output governance: partnering with Risk/Compliance on evaluation, human-in-the-loop review, and sign-off processes for agent outputs (e.g., IC memo agents, portfolio agents) before business use Third-party/LLM vendor governance: data handling requirements for external model providers (e.g., data residency, retention, training-use exclusions) as part of the LiteLLM/LangFuse observability stack AI agent registration & catalog governance: ensuring every agent registered in AURA carries documented data sources, access scope, and owner, consistent with the “register once, appear everywhere” model Auditability: ensuring LangFuse (or equivalent) observability logs meet governance and audit retention requirements for agent decisions and data usage Contributing to and maintaining the AI platform governance sign-off process with Legal, Compliance, Risk, and Cyber for new agents and use cases Stakeholder Management & Enablement Serve as the primary governance partner to Legal, Compliance, Risk, and Cyber on all data- and AI-related data governance matters Act as a key member of the E&C (Enablement and Control) governance group, helping evaluate and enable new AI use cases while ensuring appropriate controls are in place before launch Educate and enable Business AI Champions and Quant/Technical on governance requirements and self-service compliance Represent data governance in architecture review boards (ARB) for new data and AI initiatives