Sr Manager, Data Products, Analytics & AI/ML
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- Define reusable data products around consumers, decisions, trusted metrics and service expectations
- Build an AI-ready platform and move from static reporting to governed decision products
- Run the portfolio operating model, investment choices, monetization and responsible AI
Benchmark: Aven + Disney + Vanguard senior data-product leadership roles (current Sep 2026) Market refresh: product strategy and portfolio ownership for reporting, analytics and AI/ML; trusted semantic layers and metrics; AI-ready data foundations; quality, lineage and observability; self-service and conversational analytics; governance; adoption, monetization and measurable value.
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Define reusable data products around consumers, decisions, trusted metrics and service expectations
Data-product discovery, domains + service expectations
Coverage: Product Discovery / Data Product Management / Data Mesh / SLOs
Semantic layers, canonical metrics + trusted contracts
Coverage: Data Modeling / BI / Semantic Layer / Metric Governance
Build an AI-ready platform and move from static reporting to governed decision products
Lakehouse trust: quality, lineage + observability
Coverage: Data Engineering / Lakehouse / Data Quality, Lineage & Observability
Self-service, predictive + conversational analytics
Coverage: Analytics / ML Product / GenAI / Conversational BI
Run the portfolio operating model, investment choices, monetization and responsible AI
Portfolio strategy, operating model + value realization
Coverage: Product Strategy / Data Products / Monetization / Business Cases
Governance, responsible AI + investment/adoption metrics
Coverage: Data Governance / Responsible AI / Executive Leadership
Role-Readiness Proof / Capstone
Create a 12-18 month data, analytics and AI product portfolio for a real or simulated enterprise. Map consumer jobs and decision workflows; define domain data-product charters, owners and service expectations; create canonical metric contracts and a semantic-layer design; propose the lakehouse/reference architecture with quality, lineage and observability scorecards; prototype self-service or conversational analytics plus one predictive/AI decision product with an evaluation plan; define privacy, governance and responsible-AI controls; establish adoption, reuse, support-cost, retention or monetization measures; make explicit buy/build/retire choices; and finish with team/accountability model, budget/investment roadmap and executive business case.
Proof Rubric
- Demonstrate senior data-product leadership: clear consumer decisions and product boundaries
- accountable domain ownership
- trusted metric contracts
- measurable quality, lineage and reliability
- AI-ready foundations
- self-service usability
- credible predictive/GenAI evaluation
- governance and responsible-AI controls
- adoption, reuse and monetization evidence
- explicit portfolio investment and retirement tradeoffs
- scalable team/operating model
- and a decision-ready narrative connecting platform spend to customer and business outcomes