Data Analyst Product Owner

Location: New York, NY

Work model: 3 days onsite

Engagement: Long-term contract

Role overview

We are seeking a hands-on Data Product Owner to help shape and improve enterprise data products within a product-engineering environment supporting business, digital, and data capabilities.

This is a roughly 50/50 business and technical role. The successful candidate will work directly with business stakeholders and engineering teams, make priority decisions, define requirements and product backlogs, and independently query, test, profile, and reconcile data. The person should be able to investigate data issues without waiting for developers, then turn the findings into clear product decisions and executable work.

The role is best suited to a client-facing business or data analyst who can also perform core product-owner activities. It is not a roadmap-only product role, and it is not a production data-engineering role.

What you will do

Data product ownership

• Own one or more enterprise data products or data domains end to end, from discovery through delivery, adoption, and continuous improvement.

• Make priority decisions within the owned domain, balancing business value, data quality, user impact, risk, and engineering capacity.

• Define and maintain the product roadmap, backlog, user stories, acceptance criteria, KPIs, and delivery priorities.

• Translate business needs and data issues into clear requirements and actionable work for engineering teams.

• Track product adoption, data-quality outcomes, user feedback, risks, dependencies, and delivery progress.

Hands-on data investigation

• Use SQL and enterprise data platforms to query, profile, test, reconcile, and validate data.

• Investigate anomalies, inconsistencies, lineage questions, and data-quality issues independently.

• Define test cases and validation criteria for data products and changes.

• Explain findings clearly to business stakeholders and engineering teams.

• Identify root causes, document trade-offs, and recommend practical resolution paths.

Stakeholder and domain collaboration

• Partner with business SMEs across sales, finance, operations, enterprise data, governance, and engineering.

• Lead discovery sessions, working sessions, user interviews, and requirements discussions.

• Serve as a trusted bridge between business users and technical teams.

• Present product priorities, data findings, delivery risks, and decisions to senior stakeholders.

• Build strong relationships with users and help teams understand, trust, and use enterprise data products.

Data quality, governance, and AI readiness

• Own data-quality standards and outcomes for the assigned data product or domain.

• Partner with enterprise governance teams on metadata, lineage, master data, data dictionaries, and control requirements.

• Improve the structure, semantics, documentation, and usability of enterprise data so it can support AI-enabled reporting and future analytics use cases.

• Help evaluate practical uses of AI and automation that improve data investigation, reporting, and product delivery.

Delivery and operating model

• Manage dependencies, risks, blockers, and decisions across multiple stakeholders and workstreams.

• Work closely with engineers and data specialists to understand data models, pipelines, and platform constraints.

• Drive delivery with a self-starter mindset and a high level of ownership.

• Produce concise documentation, executive updates, decision materials, and business cases.

Required qualifications

• 8+ years of experience in data product management, data product ownership, data analysis, data science, business analysis, or a closely related role. Equivalent hands-on experience may be considered.

• Strong hands-on SQL and data-analysis skills, including the ability to investigate and validate data independently.

• Experience working with large, complex datasets and enterprise data platforms. Hands-on Snowflake experience is strongly preferred.

• Experience with data quality, governance, metadata, lineage, data dictionaries, or master data concepts.

• Experience defining priorities, requirements, user stories, acceptance criteria, and delivery outcomes.

• Strong stakeholder-management and communication skills, with the ability to translate complex data issues into clear business language.

• Ability to work independently, make sound priority decisions, and operate effectively across business and technical teams.

• New York-based and able to work in a hybrid model.

Preferred qualifications

• Experience in asset management, investment management, wealth management, financial advisory, capital markets, or another financial-services environment.

• Experience with client, investor, transaction, product, portfolio, position, AUM, fund-flow, trade, settlement, or post-trade data.

• Experience supporting reporting, semantic data layers, AI-enabled analytics, or trusted data products for LLM-based workflows.

• Experience working with Snowflake and related cloud data platforms.

• Background as a Data Product Owner, Data Product Manager, Data Analyst, Business/Data Analyst, Analytics Product Manager, Data Scientist, or similar hybrid role.

• Experience evaluating data tools, third-party data vendors, or data-product platforms.

What success looks like

In this role, you will be able to:

• Understand a business or data problem directly from users and SMEs.

• Investigate the underlying data yourself and establish what is actually happening.

• Make a clear priority recommendation or decision.

• Convert the finding into a well-scoped product requirement or engineering backlog item.

• Improve data quality, trust, usability, and adoption over time.

• Communicate progress and trade-offs clearly across business and technical stakeholders.