Director, Data Engineering Centre of Excellence (CoE)

About this role Mission Build a world‑class data‑engineering capability by standardising how Enterprise Data Platforms and pipelines are designed, delivered, and operated—at scale, across clouds, and with production‑grade reliability. Key Responsibilities Enterprise Data Platform (EDP) Leadership Bring proven, hands‑on experience designing and building Enterprise Data Platforms for large, complex organisations. Lead and influence end‑to‑end EDP architecture, spanning acquisition, ingestion, storage, transformation, orchestration, and distribution. Translate real‑world EDP delivery experience into reference architectures, design standards, and reusable patterns within the CoE. Data Engineering CoE Ownership Establish and lead the Data Engineering Centre of Excellence. Define enterprise‑wide standards for data ingestion, transformation, orchestration, and publishing. Set clear quality, performance, and operational readiness bars for all data‑engineering teams. Snowflake Internal Platform Skills Act as the internal Snowflake engineering authority, defining standards for: SQL design and performance‑aware query patterns Data modelling across raw, stage, standard, and publish layers Warehouse usage, multi‑cluster strategies, and cost efficiency Snowflake parameter governance (performance, stability, security) Partner with platform and DBA teams on Snowflake operations, DR, monitoring, and run‑books. dbt, Airflow & Kubernetes Define and govern best practices for: dbt project structure, testing, documentation, and promotion Airflow DAG design, scheduling, backfills, retries, and ownership Kubernetes‑based execution for data workloads, including resource management and isolation Ensure consistency, reliability, and supportability across all orchestration and execution layers. DevOps for Data Pipelines Own the DevOps strategy for data engineering across all deployed Enterprise Data Platforms. Standardise CI/CD pipelines for: dbt models and transformations Airflow DAGs and pipeline frameworks Configuration and platform changes Drive infrastructure‑as‑code and automation for repeatable environments. Embed operational reliability by design: Observability, alerting, and run‑state readiness Release safety, rollback, and recovery patterns Clear ownership, on‑call, and escalation models Multi‑Cloud Data Engineering (Azure & AWS) Define multi‑cloud data‑engineering standards across Azure and AWS. Promote cloud‑agnostic pipeline design where appropriate, while leveraging platform‑specific strengths. Partner with Cloud and Platform teams on security, identity, networking, and cost efficiency. Master Data Management & External Data Establish engineering standards for Master Data Management (MDM), ensuring mastered entities are reliable, governed, and reusable. Define compliant patterns for web‑scraping and external data acquisition, integrated into enterprise pipelines with governance and observability. Enablement & Influence Enable teams through design reviews, playbooks, reference implementations, and coaching. Act as a consultative accelerator, not a central delivery or ticket‑based team. Success Measures Consistent data‑engineering and EDP standards adopted across teams Improved platform reliability, scalability, and developer productivity Reduced operational toil and performance issues through better design Faster onboarding onto approved patterns and platforms Clear reduction in bespoke, one‑off implementations Required Experience Deep experience in data engineering, data platform engineering, or enterprise data architecture Demonstrated experience designing and building Enterprise Data Platforms for large organisations Strong hands‑on expertise in Snowflake, dbt, Airflow, Kubernetes, and data‑pipeline DevOps Proven ability to drive standards and influence outcomes in a matrixed environment Our benefits To help you stay energized, engaged and inspired, we offer a wide range of benefit ... (truncated, view full listing at source)