We are building the data and metrics foundation that our AI systems depend on. This role owns the readiness of the data engineering designs, pipeline and the BI Engineering (BI) metrics/semantic layer the trustworthy, well-defined, low-latency source of truth that every AI system consumes to deliver high-quality insights to end users.
You are the owner of the data supply chain for AI: clean, governed, well-modeled, fast-serving data and certified metrics. AI experiences including our conversational insights agent are built directly on the foundation you own. You'll partner closely with the teams building those experiences, giving them the data contracts, certified metrics, and latency guarantees they need to succeed, and closing the data and metric gaps that stand between a good answer and a great one. Your impact shows up everywhere AI touches data: the more reliable, well-defined, and fast your platform, the better every insight our users receive.
Key job responsibilities
Own data engineering readiness pipeline reliability, freshness, schema/data contracts, and the transformation layer that turns raw data into AI-consumable data products.
Own the BI metrics & semantic layer drive consistent, certified metric definitions (single source of truth) so AI systems and dashboards compute the same numbers the same way.
Guarantee high quality, low latency, set and hold SLAs for data freshness, accuracy, completeness, and query/serving latency that AI consumers require for real-time insights.
Define the AI-consumption interface data contracts, certified metric catalogs, retrieval/RAG-ready structures, and APIs the conversational agent (and future AI consumers) build against.
Partner tightly with the conversational-agent team as a primary internal customer; align roadmaps, close data/metric gaps that block agent quality, and establish feedback loops on where the agent's answers fail due to underlying data.
Establish governance metric certification process, data quality monitoring, lineage, and ownership so trust in the platform scales.
Prioritize the roadmap across pipeline modernization, metric coverage, and latency/serving improvements, balancing foundational investment against consumer needs.
Write crisp technical product docs (PRFAQs, one/two-pagers, data-contract specs) and drive alignment across Data Engineering, BI, Science, and the AI-application teams.
A day in the life
As a Technical PM bridging the gap between data and AI, you act as the critical connective tissue translating high-level business goals into scalable data architecture. You collaborate with business stakeholders and AI/ML Scientists to turn abstract requirements into concrete features, training datasets, and real-time data needs. Simultaneously, you partner with Data and BI Engineers to design low-latency feature stores, map data schemas, and standardize the business metrics conversational agents use to make decisions. Your focus remains entirely on building a robust, high-performance data foundation layer, ensuring upstream pipelines seamlessly power downstream AI logic.
About the team
Our team owns the Analytics, BI dashboards, and ML models that drive AWS Support operations across customer experience, operational efficiency, tooling, and security. We act as the strategic bridge within the organization, partnering directly with operations managers, leadership, and central support functions to deeply understand their day-to-day challenges.
From there, our team of AI/ML Scientists, Data Engineers, and BI Engineers translate those requirements into scalable data architectures, robust metrics, and automated systems. Ultimately, we build and maintain the high-performance data solutions that our internal stakeholders rely on to optimize global operations
BASIC QUALIFICATIONS
- 7+ years of working directly with engineering teams experience
- 5+ years of technical program management working directly with software engineering teams experience
- Experience developing and executing/delivering product and technical roadmaps
- Experience working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage
- 3+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
- Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems, or experience in machine learning, data mining, information retrieval, statistics or natural language processing
PREFERRED QUALIFICATIONS
- 5+ years of project management disciplines including scope, schedule, budget, quality, along with risk and critical path management experience
- Experience managing projects across cross functional teams, building sustainable processes and coordinating release schedules
- Experience defining KPI's/SLA's used to drive multi-million dollar businesses and reporting to senior leadership
