Senior Data Engineer
United Kingdom
Full Time
2 hours ago
Visa Sponsorship
Key skills
AirflowBigQueryCloudGoogle Cloud PlatformPythonSQLTerraformVaultAIMLGenAIGeminiData EngineeringdbtIntegration TestingGCPGoogle CloudPub/SubCloud StorageCloud BuildVertex AIcomposerCI/CDLeadershipMentoringCommunicationCollaboration
About this role
Role Overview
- A defining role on a high-priority, high-visibility data platform programme
- Genuine technical leadership — your architecture decisions will stand in production
- Direct exposure to GenAI infrastructure, ML platforms, and AI-era data engineering
- Collaboration with global engineering teams across three continents
- Support and funding for GCP Professional Data Engineer certification and advanced training
- A clear pathway to Lead Engineer for the right candidate
- Hybrid working from a modern campus environment
Requirements
- 5+ years of data engineering experience in production, cloud-native environments
- 5+ years of advanced SQL: BigQuery specifics, query profiling, partitioning/clustering optimisation, complex analytical queries
- 3+ years of GCP production experience: architecture design and delivery at scale
- Deep, hands-on expertise across: BigQuery, Dataflow, Cloud Composer (Airflow), Pub/Sub, Dataplex, Cloud Storage, Terraform, Cloud Build
- Mastery of data modelling methodologies: Dimensional/Kimball, 3NF, Data Vault — with real-world application of each
- Production-level Python: OOP design patterns, async processing, unit/integration testing, GCP SDK usage
- Demonstrated experience designing CI/CD pipelines for data products
- Track record of leading legacy-to-cloud migrations
- Demonstrated technical leadership: you have designed solutions, led reviews, and raised the quality bar of a team
- Proven ability to work in high-ambiguity environments and drive clarity through technical design
- Strong communication: able to write architecture decision records, run design reviews, and present to non-technical stakeholders
- Evidence of mentoring junior engineers and improving team capability
- Minimum 2:2 degree (or international equivalent) in Computer Science or related technical field; demonstrated professional experience considered in lieu for internal applicants
- GCP Professional Data Engineer certification (Desired)
- Experience designing AI/ML data pipelines — feature stores, training data pipelines, Vertex AI integration (Desired)
- Hands-on experience with vector databases or embedding pipeline design (Desired)
- Active use of AI-assisted development tools (Copilot, Gemini, Cursor) in production delivery (Desired)
- Experience with dbt Core / Dataform in a production, team setting (Desired)
- Data engineering experience in a regulated financial environment (banking, insurance, credit) (Desired)
- Experience designing event-driven architectures with Pub/Sub and Dataflow (Desired)
Tech Stack
- Airflow
- BigQuery
- Cloud
- Google Cloud Platform
- Python
- SQL
- Terraform
- Vault
Benefits
- Support and funding for GCP Professional Data Engineer certification and advanced training
- A clear pathway to Lead Engineer for the right candidate
- Genuine technical leadership — your architecture decisions will stand in production
- Direct exposure to GenAI infrastructure, ML platforms, and AI-era data engineering
- Collaboration with global engineering teams across three continents
- Hybrid working from a modern campus environment