About the job
About the Role
Join qodeworld as a Data Product Manager, where you will take charge of a diverse portfolio of 3–5 enterprise data products. This position is perfect for professionals with extensive experience in data technologies, analytics, and data governance, alongside a solid foundation in core banking processes. As the strategic lead, you will ensure that our data products align with business objectives, foster insights, and uphold compliance and operational excellence.
In this senior leadership role, you will collaborate closely with business stakeholders, data engineers, data architects, and analytics teams to deliver high-quality, governed, and scalable data products. You will also play a key role in defining the vision, roadmap, and lifecycle management of these products in accordance with our enterprise data strategy.
Key Responsibilities
Product Ownership & Strategy
· Take ownership of the entire lifecycle of assigned data products, including vision, strategy, roadmap, and delivery.
· Work in partnership with business units, analytics teams, and technology partners to prioritize features and enhancements effectively.
· Define and monitor key product success metrics and adoption KPIs.
· Champion data products across the organization, ensuring alignment with enterprise data governance and cloud/data strategy initiatives.
Stakeholder Engagement
· Serve as the primary point of contact between business stakeholders and data engineering/analytics teams.
· Translate business requirements into actionable data product specifications.
· Facilitate collaboration across functions to manage trade-offs and dependencies.
Data Governance & Quality
· Ensure data products are compliant with regulatory, security, and privacy regulations.
· Establish and maintain data quality standards, lineage, and observability metrics.
· Partner with Data Governance, Risk, and IT Security teams to ensure compliance and readiness for audits.
Technical Leadership
· Leverage modern data technologies (e.g., relational databases, data warehouses, data lakes, ETL pipelines, cloud platforms, APIs, BI tools) effectively.
· Collaborate with data engineering teams on architectural, modeling, and platform decisions.
· Assess emerging technologies and provide recommendations to enhance product offerings.
