HAPI - Product owner, Data Platform and Data Products

Job Description

Job Description

Hapi (Data Travel, LLC) is looking for a technically-minded Product Owner to join our Engineering organization and drive the strategy, prioritization, and delivery of our Data Platform and Data Products. This is a hands-on, highly collaborative role embedded within our Data Engineering team, working at the intersection of data infrastructure and business value.

You will own the end-to-end backlog for our AWS-native data platform , from raw data ingestion and pipeline orchestration through data cataloging, governance, and the delivery of APIs and insights that power both internal teams and customer-facing products. You will be the connective tissue between Engineering, Product Management, Data Analysts, Solutions Architecture, and Implementation/Support. You will translate complex technical requirements into clear, actionable work while keeping the broader roadmap aligned with business priorities.

This is not a purely strategic role. You will be in the details: co-authoring tickets with engineers, triaging data quality issues, reviewing pipeline specs, and ensuring delivery stays on track. If you thrive in a fast-paced, technical environment and care deeply about data as a product, this role is for you

Key Responsibilities

Backlog Ownership & Agile Delivery

• Own and maintain the data platform product backlog, ensuring it is well-prioritized, visible, and aligned with both technical and business objectives.
• Co-author epics, features, and user stories with Data Engineers, including clear acceptance criteria, data contracts, and technical specifications.
• Facilitate sprint planning, backlog grooming, and retrospectives in partnership with the Data Engineering team lead.
• Actively triage incoming requests from Product Management, Data Analysts, Solutions Architecture, and Implementation/Support, translating them into actionable backlog items.
• Remove blockers and escalate risks or delivery challenges proactively to the Head of Engineering.

Data Platform & Pipeline Ownership

• Define and prioritize requirements for AWS-native data ingestion pipelines, ETL/ELT workflows, and data lake architecture, in close collaboration with Data Engineers.
• Own the data catalog and governance backlog, ensuring data assets are documented, discoverable, and reliable for downstream consumers.
• Drive requirements for data quality frameworks, monitoring, and SLA definitions across platform pipelines.
• Partner with Application Engineering to ensure data products and APIs are integrated effectively into customer-facing and internal systems.

Data Product Delivery

• Translate business and analytical requirements from Data Analysts and Product Managers into clearly defined data product specifications, including datasets, APIs, and dashboard-ready outputs.
• Define success metrics for data products and monitor adoption, quality, and performance post-launch.
• Ensure data products meet the latency, reliability, and scalability requirements of a high-volume SaaS environment.

Roadmap & Stakeholder Collaboration

• Collaborate with the Product Management team and Solutions Architecture to maintain a coherent data platform roadmap, balancing near-term delivery with long-term platform health.
• Serve as the primary point of contact for Implementation and Support teams on data-related questions, bugs, and feature requests.
• Provide regular, transparent updates on platform progress, risks, and upcoming milestones to Engineering leadership and cross-functional stakeholders.
• Gather feedback from internal and external data consumers to inform continuous platform improvement.

Skills, Knowledge and Expertise

• 2-4 years of experience as a Product Owner or Product Manager with direct ownership of data engineering, data platform, or data infrastructure products.
• Hands-on experience working within Agile/Scrum teams as a PO - writing and refining tickets, running sprint ceremonies, and managing a live backlog.
• Strong working knowledge of modern cloud-native data architectures, including data lakes, lakehouses, and ELT/ETL pipeline patterns.
• Practical exper