Data Product Owner

VySystems; New York (NY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

VySystems in New York is seeking a Data Product Owner with asset and wealth management data experience. You will own data products end to end, work with business users, translate requirements into engineering deliverables, and drive stakeholder adoption across teams.

The role requires 8–10 years in data product management within financial services, hands-on data analysis, proficiency with SQL, and experience with platforms like Snowflake or Databricks.

Qualifications

• 8–10 years in data product management within financial services.
• Experience with data products and end-to-end ownership.
• Strong SQL and data quality skills.
• Excellent communication with senior stakeholders.
• Experience with investor/client data workflows.
• Knowledge of data governance and metadata management.

Responsibilities

• Own data products end to end from ideation to launch.
• Collaborate with business users to define data requirements.
• Translate requirements into engineering tasks and acceptance criteria.
• Lead build validation and UAT with stakeholders.
• Drive adoption and governance across data platforms.

Skills

Data product ownership

Data governance

SQL

Stakeholder communication

Financial services domain

Tools

Snowflake

Databricks

Azure Data Platform

Job description

Strong Asset Management domain experience is mandatory

Wealth Management experience is acceptable if the candidate has worked extensively with client/investor data and investment operations.

Candidate must be willing to work 3-4 days/week from the New York office.

Should work with business users, own data products end to end

Functional Expertise

• Experience owning or managing Client Master and Transaction Master data products.
• Strong understanding of investment data, investor/client onboarding, transactions, capital formation, investor reporting, fund operations, and related data domains.
• Hands-on experience with SQL, data profiling, data validation, reconciliation, data quality, and data analysis.
• Experience with Azure-based data platforms.
• This is not a traditional Scrum Product Owner role, this is a Data Product Owner Role

Candidate should be able to:

• Work directly with business users and understand data needs.
• Define requirements and translate them into engineering deliverables.
• Participate in build validation and UAT/testing activities.
• Drive stakeholder adoption and champion the data product.
• Coordinate end-to-end delivery across business and technology teams.

Candidates from below companies:

• Blackstone, KKR, UBS, Merylinch , Ares, Carlyle, Brookfield, Blue Owl, BlackRock, Fidelity, Wellington, Franklin Templeton and similar investment firms

8–10 years of experience in data product management, data product ownership, or a closely related data-focused role within financial services; background in investment management or wealth management is strongly preferred

Proven track record of owning and delivering multiple data products simultaneously, including full roadmap ownership and end-to-end management of data transformation programs

Strong hands-on proficiency with data analysis and querying

Demonstrated experience managing large, complex datasets across enterprise data platforms (e.g., Snowflake, Databricks, or equivalent)

Working knowledge of data governance principles, data quality management, metadata management, and data lineage practices

Excellent communication skills—written, verbal, and visual—with the ability to translate complex data concepts into clear business language and present confidently to senior and executive leadership

Highly organized and detail-oriented; able to manage competing priorities independently, hold high personal standards, and deliver quality work with minimal supervision

Proficiency with product and project management tools such as Jira, Confluence, or equivalent

Preferred

Direct domain expertise in asset management or wealth management data (e.g., portfolio and position data, investor reporting, AUM and flows, trade and settlement data)

Demonstrated experience leveraging AI tools—including generative AI assistants, LLM-based workflows, or AI-powered analytics platforms—to accelerate data product development and analysis

Experience evaluating and managing third-party data vendors