Job Description
Product Owner and Specification Engineer - Enterprise Data Platform and KMS
As part of our client's engineering organization, we are looking for Product Managers to own the outcome for slices of the data platform, the Knowledge Management Service and the Data Accelerators. Each works across several pods and is accountable for what gets built, why, and whether it moved the metric.
This role absorbs what was previously split between a business analyst and a product owner. Specification is now a core engineering input: an ambiguous requirement that a human engineer would have quietly resolved becomes a defect when an agent builds it literally. Writing unambiguous, testable specifications is the defining skill of the role.
This is not a backlog administration position and not a stakeholder liaison role. There are no sprint commitments to broker and no requirements documents to hand over. You own an outcome and the queue that produces it.
Key Responsibilities:
- Own the outcome for your slice: what we build, why it matters, and how we will know it worked.
- Shape and prioritise a flow-based backlog in GitHub Projects, with intake and roadmap managed in Aha.
- Write specifications precise, testable and unambiguous enough for an AI agent to build from without clarification, drafted with agent assistance.
- Work directly with senior business stakeholders to turn demand into sequenced, fundable work.
- Replenish the pods' queues at a steady cadence so they are neither starved nor flooded.
- Track adoption and value metrics for your slice, and let them change your priorities.
- Make scope trade-offs with the AI System Architect rather than escalating them.
About You:
The ideal candidate is a product manager who writes so precisely that engineers stop asking questions, and who can tell you the three metrics their slice is accountable for without opening a deck. They treat specification as engineering work, not paperwork. They are not a backlog administrator, a ceremony owner or a channel to the business. They decide what gets built and answer for the result.
Requirements
- Professional product ownership or product management experience for a technical or data product.
- Demonstrated ability to prioritise on evidence rather than on the loudest request, and to say no with a reason.
- Experience owning a measurable outcome rather than a delivery date.
- Strong requirement and specification writing. Your defining skill is removing ambiguity before engineers or agents hit it.
- Familiarity with structured specification approaches such as EARS, given-when-then or equivalent testable formats.
- Ability to decompose a business problem into independently deliverable increments.
- Enough technical depth to argue productively with engineers about data models, APIs and trade-offs.
- Working understanding of data platforms, pipelines and the difference between a data product and a report.
- Confident reading SQL and interpreting platform and usage metrics.
- Experience managing senior business stakeholders who do not agree with each other.
- Fluent English and the flexibility to work across India, LATAM and US time zones.
- Experience using AI assistance to draft, test and refine specifications.
- Understanding of why specification precision determines agent autonomy and rework rates.
- Comfort working in a flow-based model with no sprint commitment ritual and no PI planning.
- Preferred: Data platform or knowledge management domain experience.
- Preferred: Aha and GitHub Projects.
- Preferred: Professional services or consulting environment experience.
- Preferred: Experience with Kanban replenishment and flow-based prioritisation.
- Preferred: Prior business analyst experience alongside product ownership.
- Preferred: Experience in global, distributed engineering organizations.
Benefits
- Pods of five with end-to-end ownership. Your pod builds, tests, releases and runs its own slice of the platform. Nothing is handed to a separate QA or operations function.
- Kanban and flow rather than sprints. Work is pulled, work in progress is limited, and throughput, lead time and cycle time are measured. There is no sprint commitment ceremony and no PI planning.
- A six-phase product lifecycle: Plan, Define, Build, Review, Release and Support.
- Tooling: GitHub for Projects, Issues, Actions and GitOps; Factory.ai and GitHub Copilot for agentic engineering; Nullplatform for environments; Aha for portfolio intake.
- Agent-first delivery. We target 60 to 70 percent agent autonomy on eligible work and rework below 15 percent, which shifts engineering effort from typing code to specifying and reviewing it.