Senior Data Product Manager - Banking

Data Product Owner

Tiger Analytics is an advanced analytics consulting firm recognized for our deep expertise in Data Science, Machine Learning, and AI. Our partnerships with Fortune 100 companies enable us to tackle complex business challenges and drive value through innovative analytical solutions. We are looking for an experienced Data Product Owner who can bridge the gap between source system teams, data engineering, business stakeholders, and downstream data consumers.

The ideal candidate will have strong experience working on enterprise data projects and a solid understanding of modern cloud data architectures, data products, data quality, data contracts, and engineering delivery practices.

Responsibilities:

• Partner with source-system owners, SMEs, and technology teams to understand modern cloud-based Systems of Record (SORs) and the data they produce.
• Understand and document source systems, data attributes, business definitions, relationships, rules, constraints, and dependencies.
• Identify the types of business insights, KPIs, metrics, and downstream use cases that can be supported by the underlying data.
• Develop a strong understanding of data availability, SLA, latency, timeliness, and freshness requirements for downstream data products.
• Understand source-system integration options, connectivity requirements, access mechanisms, and associated security considerations.
• Translate source-system knowledge into clear and actionable requirements for data engineering teams.
• Work closely with Data Engineers, Architects, Developers, QA, and other technical teams on a day-to-day basis.
• Validate technical assumptions and clarify requirements, dependencies, priorities, and acceptance criteria.
• Prioritize data product development tasks and help engineering teams remove blockers.
• Facilitate communication between engineering teams and source-system SMEs for connectivity, access, sample data, data availability, and technical dependencies.
• Define and enable appropriate data testing and validation strategies.
• Support engineering teams throughout development, testing, deployment, and production release.