At a glance

• Client: FTSE-equivalent global retailer — client name available on request
• Programme: Platform enablement across the enterprise data & analytics organisation — an “enabling team” in Team Topologies terms
• 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 — systematically sourced from what actually slows teams down, prioritised by aggregate impact, 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 organisation's single, prioritised view of cross-team technical blockers.
• AI support chatbot live for platform support requests.
• Cost transparency, artifact management, and data-quality observability initiatives 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.