Requirements

• Minimum of 10 years experience in Data & Analytics, including Staff or Principal level roles in Data Engineering, Data Architecture, or Data Platform.
• Extensive hands-on experience designing, building, and operating cloud data platforms on Snowflake and AWS.
• Proven track record leading complex, large-scale migrations from legacy systems.
• Experience designing data products across source-aligned, aggregate, and consumer-aligned patterns.
• Ability to architect and scale modern cloud data platforms using Snowflake, Apache Iceberg, Amazon Redshift, and S3.
• Hands-on expertise in developing data transformation pipelines and orchestrating complex workflows using dbt and Apache Airflow.
• Strong experience automating infrastructure and deployment pipelines using Infrastructure as Code tools like Terraform and GitHub Actions.
• Ability to implement data governance, technical catalogs, end-to-end lineage, and data product portals to maximize data observability.
• Experience leveraging AI tooling to accelerate data engineering workflows, migrations, and platform readiness for AI applications.
• Experience establishing or leading a community of practice and driving standards adoption across teams.
• Proven track record of cross-functional partnership with Product, Engineering, Data Science, and Governance teams.
• Ability to mentor and train technical staff across the company on advanced AI system creation.
• Capability to architect custom AI solutions and smart automated systems that are safe, maintainable, and mission-aligned.

What you'll be doing

• Own the Data Platform strategy and lead a multi-phased roadmap for migrating to the Future State Data Platform on Snowflake and AWS.
• Advise on migration architecture, transition states, historical data migration, and legacy system decommissioning.
• Lead architecture, design, and development of scalable cloud data warehouse, lakehouse, streaming, and analytics solutions.
• Define data ingestion, transformation, and orchestration patterns, naming conventions, and boundaries between Product and Data Engineering.
• Identify and resolve architectural bottlenecks, control weaknesses, and technical debt within the data ecosystem.
• Own and develop standards for the Data as a Product Community of Practice, covering metadata, data contracts, versioning, data quality, SLAs, and semantic layers.
• Champion data quality, lineage, observability, and governance with automated checks and monitoring in production.
• Participate in the Data Architecture Review Board, holding decision rights over data architecture proposals.

Perks and benefits

• Flexible-first working approach with hybrid office presence in London (2 days in office).
• Competitive salary and benefits package.
• Benefit allowance tailored to personal life and family needs.
• Private medical and dental insurance, health assessments, and access to a digital GP.
• Life assurance, share schemes, and financial coaching.
• Dedicated annual learning allowance for professional development.
• Electric car and cycle-to-work schemes, plus season ticket loans.
• Award-winning parental leave policies supporting fertility treatments and family additions.
• Access to barista coffee and subsidised Just Eat lunches in office.
• Opportunity to join one of The Sunday Times Best Companies to Work For 2026.