Data Technical Lead

As Data Platform Tech Lead, you design and deliver scalable, secure data platforms that empower analytics, AI, and modern use cases. You will lead data engineering teams and guide complex delivery across client projects within PA’s Digital & Data community. You collaborate with cross-functional experts to drive digital transformations and establish robust data governance, quality, and observability. You’ll mentor engineers, influence architectural choices, and help teams adopt GenAI responsibly to deliver measurable impact.

• Private healthcare
• 25 days annual leave (+ half day Christmas Eve)
• Generous pension
• Community and charity involvement
• Annual performance bonus
• PA share ownership

• Lead data engineering or data platform teams and set direction for delivery
• Design and deploy scalable, reliable data platforms across ingestion, storage, processing, modelling, orchestration and serving
• Apply software engineering practices to data platforms (testing, CI/CD, IaC)
• Establish data quality, observability, governance, metadata and security practices
• Lead modernization and migration of data platforms, including cutover and decommissioning
• Make pragmatic technology choices and balance trade-offs across platform, storage, and processing
• Shape end-to-end engineering lifecycle for data and software products, including build, test, deploy, operate
• Communicate clearly with engineers, clients and senior stakeholders and establish credibility quickly
• Provide technical input into bids, proposals, and client discussions to shape approaches

• Technically led data engineering or data platform teams
• Designed and delivered modern data platforms that are scalable, reliable and cost-effective in enterprise environments
• Made architectural decisions across ingestion, storage, processing, modelling, orchestration and serving
• Applied strong software engineering practices to data platforms (testing, CI/CD, IaC)
• Established data quality, observability, governance, metadata, and security approaches
• Led data platform modernization and migration including coexistence and cutover
• Shaped end-to-end engineering lifecycle for data and software products
• Excellent communication with engineers, clients, and stakeholders and ability to operate in ambiguity
• Contributed technical input to bids and implementation plans

• Cross-functional collaboration
• Clear communication
• Mentorship and coaching
• Cloud platforms: AWS, Azure, GCP
• Databricks, Snowflake, cloud-native data services
• Data architecture: lakehouse, medallion, data mesh