Translate customer and business needs into clear product requirements, data contracts, schemas, and measurable KPIs
Define how downstream analytics, dashboards, and insight layers are designed to ensure data is interpretable and commercially useful
Drive an AI-first product roadmap by identifying where classical ML models (regression, trees, clustering, forecasting) versus GenAI approaches (embeddings, retrieval, vector search, LLM-based reasoning) create the most value
Partner with AI, data engineering, and platform teams to ensure data is structured, enriched, and reliable for these models
Establish and maintain data governance, lineage, quality, and trust frameworks across the platform
Lead multi-disciplinary squads spanning Product, Data Engineering, AI, and Integrations, operating as a senior product owner
Work closely with engineering leadership on scalability, performance, and reliability of ingestion and transformation pipelines
Good to have:
59 years of experience in data products or data platform product roles
Having exposure of electronics, OEMs, EMS, Semiconductors is mandatory.
Mandatory experience building or scaling B2B SaaS products, ideally enterprise-grade
Strong understanding of classical ML techniques and modern GenAI architectures, with clear judgement on practical application
Solid grasp of data modelling, ETL / ELT principles, integration patterns, and modern data stacks
Experience defining visualization layers and working with BI teams to formulate dashboards and insight products on the platform
Proven ability to work effectively with engineering, data, and AI teams
Strong communication skills, with the ability to translate technical concepts for business stakeholders
Experience leading squads in fast-moving, high-growth environments
Exposure to supply chain, procurement, or industrial data products is a plus
