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In close collaboration with B2B product squads, platform engineering, integration teams, and SRE, the Data & AI Assets Engineering Lead – B2B acts as the technical and engineering owner of reusable B2B Data, AI, and Agentic assets.
As a player-coach, the role is both hands-on and structurally accountable: it designs, builds, and runs production-grade B2B assets (Gold datasets, features, agents, knowledge packs), while coaching teams to engineer for reuse, quality, and operability by default across business lines and geographies.
• Own B2B Data & AI assets end-to-end (design build run evolve)
• Define and maintain B2B Gold models, features, agent inputs, and knowledge assets
• Ensure assets align with canonical models, metadata standards, and promotion rules
• Build assets with production-grade guarantees (SLAs, observability, cost )
• Actively contribute hands-on to complex modeling, pipelines, and AI logic
• Ensure reuse across B2B use cases, preventing duplication and fragmentation
• Coach engineers and squads on asset-first, reuse-by-design practices
• Partner with SRE to embed operability, reliability, and cost control into assets
• Coordinate with Integration COE on data contracts and source quality
• Support fast-track experiments & ensure clean promotion to certified assets
• Act as a standing contributor to the Design Authority
• Credible experience with B2B/CRM related use cases – ideally Sales & Marketing related
• Strong background in data modeling and analytics engineering
• Solid exposure to AI / GenAI concepts (features, RAG, agent consumption)
• Experience designing shared, reusable data assets at scale
• Comfortable with modern data stacks (Databricks, SQL, pipelines)
• Production mindset: quality, lineage, observability, cost awareness
• Experience with Agentforce/Data360 would be a plus
• Hands-on contributor on critical assets and complex problems
• Coaches teams through patterns, reviews, and examples
• Raises engineering and modeling standards through practice, not control
• Able to influence product squads without owning their backlog
• Clear communicator with product, data, and platform stakeholders
• Pragmatic, domain-aware, and reuse-oriented
• Comfortable arbitrating trade-offs between speed and standardisation
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