Data Product Manager, Client Data

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

Job Description

The Data Product Manager, Client Data is a senior role within the Technology & Engineering organization, responsible for owning and driving strategy, roadmap, and delivery for core client data products. 

This role sits at the intersection of Data, AI, and Investor Operations, with a strong focus on client data across the investor lifecycle. The Data Product Manager will act as the primary face of the Data & AI team for assigned domains, partnering closely with Relationship Management, Investor Relations and Investor Operations stakeholders, technology teams, and external service providers to deliver scalable, high-impact data products. 

The ideal candidate brings deep domain experience within the asset management investor lifecycle, strong product management discipline, and a working understanding of modern data delivery platforms. This individual will be comfortable translating complex client concepts into clear data product requirements, identifying opportunities for automation and reporting, and driving continuous improvement across data operations. 
 

Reporting relationships:

Reports to:  Head of Data Products, Data & AI 

Primary functions and essential responsibilities: 

Product Strategy & Roadmap Ownership 

• Own the data product vision and roadmap for assigned Client data domains, aligned with the Data & AI strategy and business priorities. 

• Partner with Investor Operations and Client Technology leadership to understand business objectives and data requirements, translating them into actionable data product initiatives. 

• Define clear data product goals, success metrics, and prioritization frameworks to balance near-term delivery with long-term platform evolution. 

• Serve as the primary data product owner for client data initiatives, ensuring clarity of scope, requirements, and outcomes. 

Client Data Domain Leadership 

• Act as a domain expert across client data, including pre-close investor pipeline, post-close investor capital events and balances, and investor reference data.

• Demonstrate strong understanding of the investor lifecycle across investor classes.

• Contribute to the ongoing implementation and enhancement of the core data marketplace platform for data sourcing, processing and delivery. 

• Work closely with AI teams supporting the data delivery to AI use cases, including agentic workflows, LLMs and other traditional machine learning needs.

Delivery Execution & Cross-Functional Collaboration 

• Lead discovery and requirements sessions with Relationship Managers, Investor Relations, Investor Operations and Fund Accounting to translate data needs into product capabilities (gold tables, APIs, semantic layers, feature sets).
• Partner with Business Analysts, Engineers, and QA teams to translate investor data requirements into detailed user stories, acceptance criteria, and prioritized backlogs
• Act as the primary liaison between RM, IR and Investor Operations team stakeholders and Data Engineering delivery teams, ensuring alignment, transparency, and timely issue resolution. 
• Collaborate with data engineering to design and ship pipelines using Databricks (Delta Lake, Spark, Jobs/Workflows, Unity Catalog), ensuring reliability and scalability.
• Lead training and end user enablement workshops. Identify opportunities to increase user engagement and value generated from data products.
• Establish data quality standards, define SLAs/SLOs, data contracts, reconciliation controls, validation rules, and monitoring for critical datasets.
• Implement domain-aligned governance (entitlements, lineage, auditability) with Unity Catalog and ensure compliance with internal and regulatory requirements.
• Improve data usability: define “consumer-ready” standards, documentation, data dictionaries, sample queries, and onboarding paths.
• Measure product impact: track adoption, data freshness/accuracy, time-to-insight, and business outcomes (e.g., improv