Job Description:

The Product Owner will serve as the execution link between product strategy and data engineering delivery, translating requirements into well-defined user stories and guiding the team through each sprint from refinement to release.

This role sits on a team responsible for the company's centralized data platform and the foundation for multiple client-facing data products, including dataset delivery feeds, analytics and reporting solutions, and audience intelligence tools for financial institutions. The platform ingests and models data from core banking, payments, digital banking, and CRM systems, forming the foundational layer that powers a broader suite of data intelligence products across the organization.

Working alongside the Product Manager, this role is expected to contribute data engineering domain knowledge and modern data stack awareness. The right candidate understands current architectural patterns and industry trends well enough to evaluate tradeoffs, ask good questions during technical planning, and bring informed perspective into prioritization decisions.

Responsibilities

• Partner with the Product Manager and stakeholders to define and maintain business requirements, use cases, and acceptance criteria

• Develop a working understanding of the data platform architecture, ingestion patterns, data models, and governance frameworks, sufficient to scope work accurately and ask the right questions

• Translate product and business requirements into well-scoped Epics and User Stories; maintain a healthy, prioritized product backlog

• Lead sprint planning and refinement sessions; work with engineers, analysts, and QA to scope work and estimate effort

• Collaborate with technical leads to ensure solutions meet platform standards, data quality requirements, and governance policies

• Write SQL queries and data transformation scripts to validate data, explore datasets, and support acceptance testing

• Create clear acceptance criteria for QA to use in functional and data validation testing

• Help define and maintain data quality standards, governance policies, and data taxonomy for the centralized data platform; monitor data quality across ingestion and modeling pipelines and work with engineering and analytics teams to remediate issues and uphold those standards.

• Work with Design on user flows for internal and client-facing product surfaces

• Produce business and technical documentation for stakeholders across product, engineering, and go-to-market functions

• Stay informed on trends in data platform architecture, data-as-a-product, and the modern data stack; bring relevant market context into planning and roadmap conversations

• Ensure solutions meet both business needs and technical requirements throughout the SDLC

• Use innovative approaches and AI tools to drive productivity and efficiency at a personal and team level

Must Have

• Bachelor's degree in computer science, information systems, data engineering, or a related technical field

• 3 - 5 years of experience as a Product Owner, Business Analyst, or similar role in a data-focused, software development environment

• SQL proficiency, including writing queries, interpreting transformation logic, and validating data in a warehouse environment and the ability to independently query, trace, and validate the output of data engineering and analytics work.

• Hands-on experience with cloud data warehouse platforms (e.g., Snowflake)

• Experience with data governance concepts in practice, including data quality frameworks, data lineage, metadata management, or data catalog tooling.

• Conceptual familiarity with modern data architecture patterns — data lakehouse, data mesh, ELT pipelines, data modeling approaches — and awareness of where the industry is heading

• Experience managing a product backlog and driving sprint planning and refinement in an Agile environment

• Strong written and verbal communication skills — able to translate technical concepts for business audiences and business requirements for engineering teams

• Experience in a SaaS environment translating user needs into user stories and prioritizing work for engineering

Nice to Have

• Experience in financial services or fintech, with exposure to core banking, payments, digital banking, or CRM data structures

• Experience with dbt, Apache Spark, or similar transformation and pipeline tools

• Experience with Oracle or other enterprise relational database systems

• Experience delivering data feed or dataset products to external clients

• Experience using BI tools (Tableau, Power BI, Domo) for data visualization and reporting

• Experience with Jira, Azure DevOps, or similar project tracking tools

• Exposure to AI-assisted workflows or AI-driven data tooling in a product context