About

Engineer-turned data leader delivering 60% faster cycles and 30% lower cost.

I’m Ian Crowther, a Data & AI leader who grew from Node.js engineering into strategy and team building. I modernise legacy estates into governed, product-led, AI-ready platforms—unifying data across domains and embedding Data-as-a-Product so teams deliver trusted insights and AI quickly and safely. I scale teams and operating models to compound productivity gains. I’ve built single sources of truth, aligned data producers and consumers, and established mechanisms to manage cross-domain dependencies across globally distributed teams.

I build trust in data by ensuring quality at source (100% test coverage for critical pipelines and clear ownership); enforcing SOX-compliant change and incident processes (≈20% fewer incidents); improving reliability through monitoring, alerting and SLOs that enhance MTTD/MTTR and reduce escalations; and providing governed access with role-based controls, lineage and catalog for auditability and safe self-service.

Where I drive outcomes

• Data Foundations — FinOps, platform, pipelines, quality, lineage, governance, catalog.
• Data Pillars — DataOps, data platform, data engineering, analytics engineering, feature stores, ML & generative AI.
• Enablement — Self-service access, semantic layers, AI-ready design.

Outcomes Delivered

• Hyper-growth — Scaled the organisation from 12 → 110+ in four years; authored the career framework (levels, competencies, progression criteria) and served as a mentor so people knew how to progress, driving internal promotions and strengthening culture & retention. ,
• Data Estate Modernisation — Migrated Python pipelines to Databricks, cutting cycle time 60% and infrastructure cost 30%, enabling faster delivery and a lower TCO.
• Platform Reliability — Implemented end-to-end monitoring and alerting with clear SLOs/SLAs, improving MTTD/MTTR with ~20% fewer incidents; authored the support & incident management process to standardise response and recovery.
• Governance — Established ownership, lineage, access controls, and quality policies to increase stakeholder trust and auditability, reducing escalations and enabling safer change.
• Data as a Product — Delivered a Single Source of Truth across domains, driving ~$8–10m productivity gains by eliminating analyst-side rework (no double-handling of transformations) and removing duplicate definitions, resulting in a 40% reduction in support/reporting overhead.
• AI Enablement — LLM-assisted model & query generation plus quality guardrails delivered ~40% faster pipelines, fewer defects, and 100% test coverage.
• FinOps — Cost transparency enabling better budgeting and cost-aware decisions across teams, improving forecast accuracy and spend accountability.

Leadership

Vision, outcomes, and operating models that scale globally.

Strategy

Roadmaps that mature data organisations and operationalise AI for measurable efficiency gains.

People

Scaling teams, building high-trust cultures, and creating clear growth paths.

Data

Governed, observable, AI-ready, from ingestion to insight

Software

Shipping dependable systems with modern engineering and cloud-native practices.

DevOps

Automation, reliability, and scalable infrastructure.