Data Product Manager, Investments Data

Ares Operations•New York, NY

4d•$250,000 - $300,000

About The Position

The Data Product Manager, Investments Data is a senior role within the Technology & Engineering organization, responsible for owning and driving strategy, roadmap, and delivery for core investment data products. This role sits at the intersection of Data, AI, and Investments, with a strong focus on investment data across the investment lifecycle. The Data Product Manager will act as the primary face of the Data & AI team for assigned domains, partnering closely with Investment and Operations stakeholders, technology teams, and external service providers to deliver scalable, high-impact data products. The ideal candidate brings deep domain experience within asset management investments, strong product management discipline, and a working understanding of modern data delivery platforms. This individual will be comfortable translating complex investment concepts into clear data product requirements, identifying opportunities for automation and reporting, and driving continuous improvement across data operations.

Requirements

Bachelor’s Degree in Computer Science, Engineering, Information Systems, Finance, or a related field.
5+ years in data product management, analytics, or closely related roles (or equivalent experience leading data platform initiatives).
Strong knowledge of asset management investment data (e.g., deal pipeline, trades, holdings/transactions).
Hands-on experience delivering on Databricks (Lakehouse concepts, Delta Lake tables, Jobs/Workflows, notebooks, Unity Catalog governance).
Ability to define product requirements with technical depth (schemas, SLAs, lineage, quality rules) while staying outcome-focused.
Experience with modern data engineering patterns: ELT/ETL, CDC, orchestration, data modelling (dimensional and/or Data Vault), and lifecycle management.
Excellent stakeholder management and communication skills; ability to influence without authority.
Experience collaborating with engineering teams on system enhancements, integrations, and data-driven solutions.
Strong understanding of data engineering concepts and investment systems integration, including ETL processes, data warehousing, and reporting platforms.
Strong investment domain expertise combined with disciplined product management skills.
Excellent communication and stakeholder management abilities, with the confidence to engage senior Investment and Operations professionals.
Ability to translate complex investment concepts into clear, structured data product requirements.
Analytical mindset with a focus on data quality and delivery.
Proactive problem-solver with an eye for continuous improvement.
Comfortable operating in a fast-paced, evolving environment with multiple stakeholders and dependencies.
Expertise in Agile project management methodologies, with hands-on experience managing sprints, backlogs, and iterative delivery cycles.

Nice To Haves

Advanced degree in a Computer Science field preferred.
SQL fluency preferred; comfort reading Python code and pipeline logic (not necessarily writing production code daily).

Responsibilities

Own the data product vision and roadmap for assigned Investment data domains, aligned with the Data & AI strategy and business priorities.
Partner with Investment 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 investment data initiatives, ensuring clarity of scope, requirements, and outcomes.
Act as a domain expert across investment data, including deal pipeline and origination, sponsors, investment holdings and portfolio company monitoring data.
Demonstrate strong understanding of the investment lifecycle across alternative asset classes, including public and private credit, private equity, real estate, secondaries and similar strategies.
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.
Lead discovery and requirements sessions with Deal Team members, Portfolio Managers and analysts and Operations team members to translate data needs into product capabilities (gold tables, APIs, semantic layers, feature sets).
Partner with Business Analysts, Engineers, and QA teams to translate investment data requirements into detailed user stories, acceptance criteria, and prioritized backlogs
Act as the primary liaison between Investment and 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., improved reporting timeliness, reduced manual reconciliations).
Lead backlog grooming, sprint planning, and release coordination to ensure predictable, high-quality delivery.
Ensure agile practices are applied consistently, driving transparency, predictable delivery, and continuous improvement across investment data initiatives.
Identify opportunities to improve investment reporting, data quality, and transparency across investment data products.
Drive initiatives to automate data delivery workflows and reduce operational risk through improved data platform design and integration.
Partner with Investment and Operations leadership to assess emerging capabilities, including analytics and AI-enabled solutions, where appropriate.
Serve as a visible and credible representative of Data & AI Technology initiatives to Investment and Operations leadership, including Deal, Portfolio Monitoring, Investment Accounting, and Investment Operations Data teams.
Communicate product plans, progress, dependencies, and trade-offs clearly to both technical and non-technical audiences.
Support data governance forums, steering committees, and working sessions related to investment data delivery and prioritization.