Product Manager, Data Platform
The Product Manager, Data Platform will help define and execute the roadmap for the data elements and AI framework that power aPriori’s products — including the APIs that expose manufacturing insight, the dashboards that make it visible, and the user-facing AI tools that help customers make faster, better decisions.
This role requires a mix of curiosity, technical interest, and customer empathy. You’ll help ensure that the data and AI capabilities behind our products are accurate, well-governed, and genuinely useful — supporting the internal product teams and enterprise customers who depend on them.
This is a cross-functional role for someone who can bridge technical depth and business context — someone who understands data infrastructure trade-offs, speaks fluently with data engineers and ML practitioners, and translates those capabilities into clear priorities and measurable outcomes.
Location: Belfast, UK (Hybrid)
Responsibilities
aPriori’s Cloud Data Platform
- Own the product roadmap for aPriori's data management strategy, including data services, performance, cost, and data availability requirements.
- Partner with data engineering to ensure the platform is reliable, scalable, and optimized to serve both real-time analytical workloads and AI/ML feature pipelines.
- Define data quality standards, SLAs, and observability requirements; champion a culture of trustworthy data across source and consumer data products.
Analytics & Business Intelligence
- Drive the vision and roadmap for aPriori's analytics layer, ensuring dashboards and supporting data products can be built repeatably and with ease.
- Oversee the analytics data model as a governed, reusable semantic layer — balancing self-service flexibility with data integrity.
- Stay current on analytic platform capabilities, including AI-assisted analytics features, and evaluate how they can extend aPriori's analytics experience.
aPriori’s AI Framework
- Own the product roadmap for the core services and frameworks supporting AI powered apps and agents within the aPriori platform.
- Collaborate with engineering and data science to build and iterate on prompt pipelines, context engineering strategies, and model evaluation frameworks that measure real-world AI performance, not just benchmark scores.
- Define guardrails, responsible AI requirements, and human-in-the-loop checkpoints that ensure AI outputs are safe, auditable, and trustworthy before reaching customers.
Agentic Workflows
- Define requirements for the orchestration layer that inform how agents are structured, how they hand off between steps, how failures are handled, and how the system scales.
- Define requirements for evaluations that measure agentic workflow reliability, task completion quality, and cost efficiency — establishing quality gates before production deployment.
- Work with stakeholders to support the highest-value agentic use cases: workflow automation, intelligent data summarization, proactive manufacturing cost insights, and beyond.