Google Cloud Platform Data Engineer

Location: Hybrid; 2 days per week onsite in Bristol, London, or Manchester

Salary: £65,000 to £85,000 (Dependent on Experience) + Benefits

Security Clearance: Due to the sensitive nature of the client’s programme, candidates must hold active SC Clearance or be eligible to undergo the vetting process. Please note that specific client security restrictions mean this role is strictly restricted to Sole British Nationals.

We are seeking experienced GCP Data Engineers at both Senior and Principal levels to help lead, design, and deliver high-impact cloud data platforms for our clients.

Whether you are focused on constructing resilient, production-ready pipelines or driving end-to-end solution architecture, team leadership, and client engagement, these roles offer direct ownership over complex Google Cloud environments.

• Deliver Core GCP Solutions: Develop, test, and deploy scalable batch and streaming data processing jobs using Big Query, Dataflow, Dataproc, and Pub/Sub
  .
• Automate & Orchestrate: Construct resilient automated workflows using Cloud Composer alongside robust CI/CD deployment pipelines.
• Ensure Data Quality & Governance: Implement automated testing, monitoring, alerting, and quarantine processes so downstream analytics and business users can rely on the data.
• Lead Technical Delivery: Shape software development lifecycle standards—driving best practices across architecture, code quality, testing frameworks, and release management.
• Collaborate & Align Stakeholders: Translate business goals into solid technical specifications and lead discussions with technical teams and senior client stakeholders to secure buy-in.
• Coach & Mentor: Share specialist knowledge across the team, supporting engineers of all levels and fostering a culture of continuous learning.

• Hands-on GCP Suite
  
  Experience:
  
  Proven track record building production-grade solutions using Big Query, Dataflow, Dataproc, Pub/Sub, and Cloud Composer
  .
• Frameworks & Storage: Strong understanding of batch/streaming tools (e.g.,
  Apache Spark, Apache Beam
  ) and when to apply different storage paradigms (relational, columnar, document, data lakes).
• Modern Best Practices: Active practice with version control (Git), automated testing, CI/CD pipelines, and Agile (Scrum/Kanban) methodologies.
• Communication: Ability to explain technical concepts clearly to non-technical business partners and consult pragmatically on architecture trade-offs.

• Migration of enterprise-scale legacy platforms (e.g., Hadoop, traditional RDBMS) to GCP.
• Exposure to streaming platforms like Kafka
  , MLOps workflows, or data governance tools like Dataplex and Cloud Data Catalog
  .