At a glance

• Client: FTSE-equivalent global retailer, client name available on request
• Programme: Platform enablement across the enterprise data and analytics organisation, an "enabling team" in Team Topologies terms
• Reach: 25+ product teams across regions
• Scope: Cross-team enablement backlog spanning cost transparency, engineering tooling, data-quality observability and AI-assisted support
• Duration: Ongoing, running alongside programme delivery of the enterprise data migration
• Role: Senior Delivery Manager, platform enablement delivery lead

Context

After re-establishing predictability on the enterprise data migration, my remit expanded into a platform enablement team, chartered to improve the technical roadmap for every product team in the organisation, not just one programme. The team exists to understand what slows product teams down (infrastructure blockers, tooling gaps, deployment friction) and to clear those blockers centrally, once, instead of letting every team pay the cost separately.

The challenge

Every product team was hitting the same walls independently: no fine-grained view of platform costs, fragmented engineering tooling, inconsistent data-quality monitoring, and a steady stream of support requests pulling platform engineers away from roadmap work. Nobody owned the aggregate problem, so it compounded quietly across the whole organisation.

What I did

• Established the enablement backlog as the single funnel for cross-team blockers: sourced from what actually slows teams down, prioritised by aggregate impact rather than by whoever asked loudest, and delivered through PI planning cadence.
• Leading the rollout of fine-grained cost transparency, designing governed access to Databricks system tables (Unity Catalog design, RBAC, data persistence) so every team can see and manage the true cost of its workloads.
• Delivering enterprise tooling enablement, including a JFrog Artifactory SaaS implementation via Azure Marketplace, taken through commercial validation and Enterprise Architecture / Design Authority approval.
• Standardising in-pipeline data-quality observability, surfacing DQ results in Datadog as the organisation's observability platform and driving the long-term tooling decision across Soda and Databricks DQX.
• Introduced an AI chatbot for platform support requests, deflecting routine queries so platform engineers stay on roadmap work and product teams get unblocked faster.

Outcomes

• Enablement backlog operating as the single, prioritised view of cross-team technical blockers for 25+ product teams across regions.
• AI support chatbot live for platform requests across ten platform-dependent teams.
• Cost transparency, artifact management and data-quality observability in active delivery across all product teams.
• Remit expanded within the same engagement, from single-programme delivery to organisation-wide enablement.

The full case study is available on request. Get in touch and I'll send it directly.