Senior Data Product Manager - Banking

Tiger Analytics Inc.•McLean, VA

About The Position

Tiger Analytics is an advanced analytics consulting firm recognized for its expertise in Data Science, Machine Learning, and AI. Through partnerships with Fortune 100 companies, the firm addresses complex business challenges and delivers value through analytical solutions. The Senior Data Product Manager - Banking role is for an experienced Data Product Owner who bridges source-system teams, data engineering, business stakeholders, and downstream data consumers. The position works on enterprise data projects and requires knowledge of modern cloud data architectures, data products, data quality, data contracts, and engineering delivery practices. The role offers significant career development opportunities and the chance to work in a small, fast-growing, challenging, and entrepreneurial environment with a high degree of individual responsibility.

Requirements

10 or more years of experience working on enterprise data, analytics, data product, or technology projects.
Strong experience working with data engineering teams and enterprise data platforms.
Experience with source systems or Systems of Record, including data attributes, business rules, constraints, and downstream use cases.
Strong understanding of modern cloud data architecture and the data lifecycle.
Hands-on understanding of data ingestion, ETL/ELT, Bronze/Silver/Gold architecture, data quality, data catalogs, data lineage, data contracts, and data testing.
Experience working with Agile/Scrum engineering teams.
Understanding of CI/CD, DevOps, release management, and testing practices for data platforms.
Strong requirements gathering, documentation, prioritization, and stakeholder management skills.
Ability to work effectively between business stakeholders and technical engineering teams.
Strong analytical and problem-solving skills.

Responsibilities

Partner with source-system owners, SMEs, and technology teams to understand modern cloud-based Systems of Record and the data they produce.
Document source systems, data attributes, business definitions, relationships, rules, constraints, and dependencies.
Identify business insights, KPIs, metrics, and downstream use cases supported by underlying data.
Understand data availability, SLAs, latency, timeliness, and freshness requirements for downstream data products.
Understand source-system integration options, connectivity requirements, access mechanisms, and security considerations.
Translate source-system knowledge into actionable requirements for data engineering teams.
Work day to day with Data Engineers, Architects, Developers, QA, and other technical teams.
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 about 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.