Data Engineer-Must have strong GCP experience-Inside IR35

We are working with a client delivering a large-scale Data Strategy programme and are looking for a skilled Data Engineer to help shape the next generation of their data platform.

You will play a key role in designing and building scalable, resilient and reusable data pipelines on Google Cloud Platform (GCP), enabling faster and more reliable data delivery across multiple product teams.

This is a great opportunity for someone who combines strong technical expertise with a collaborative approach, and enjoys working in complex, enterprise-scale environments with high standards for quality and reliability.

• Design and build scalable, reusable data pipeline templates across multiple data domains
• Standardise ingestion and transformation using configuration-driven frameworks
• Embed data quality checks by default (schema validation, completeness, freshness, thresholds, alerting)
• Improve pipeline resilience, monitoring, observability and recovery mechanisms
• Integrate AI/ML capabilities where appropriate (e.g. anomaly detection, intelligent monitoring)
• Support delivery of a wider Data Strategy programme, improving consistency and data quality
• Ensure pipelines are production-ready, documented and suitable for BAU handover
• Collaborate closely with Data Product, Analytics Engineering and Data Science teams

Programming & Data Engineering

• Strong experience with Python, Scala and SQL
• Solid background in data modelling and ETL/ELT pipelines
• Experience building enterprise-grade data solutions

Cloud & Platform

• Hands-on experience with Google Cloud Platform (GCP), ideally including:
  • BigQuery
  • Dataflow
  • Pub/Sub
  • Composer
  • Cloud Run / App Engine
• Experience with CI/CD, automated testing and infrastructure as code

Data Quality & Monitoring

• Experience implementing data quality frameworks, observability tooling and monitoring solutions

• Building reusable pipeline frameworks for large, multi-domain platforms
• Delivery within enterprise data transformation programmes with strong SLAs
• Exposure to AI-enabled data pipelines (e.g. anomaly detection, intelligent monitoring)
• Experience with data quality at scale
• Understanding of MLOps or deploying ML models into production

• Strong hands-on experience with GCP, Python and Scala
• Proven track record delivering production-grade data pipelines
• Deep expertise across modern cloud data platforms
• Strong problem-solving mindset and willingness to learn