Data Engineer (GCP)

Xcede
City of London
3 weeks ago
Applications closed

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Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

GCP Data Engineer
Hybrid 2/3 days - London
OUTSIDE IR/35

As a Data Engineer, youll design, build, and operate scalable, reliable data pipelines and data infrastructure. Your work will ensure high-quality data is accessible, trusted, and ready for analytics and data science - powering business insights and decision-making across the company.

What youll do Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations
Develop and evolve scalable data architecture to meet business and performance requirements
Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics
Implement best practices for data quality, testing, monitoring, lineage, and reliability
Optimise workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimisation, partitioning strategies)
Ensure secure data handling and compliance with relevant data protection standards and internal policies
Contribute to documentation, standards, and continuous improvement of the data platform and engineering processes

What makes you a great fit 5+ years of experience as a Data Engineer, building and maintaining production-grade pipelines and datasets
Strong Python and SQL skills with a solid understanding of data structures, performance, and optimisation strategies
Familiarity with GCP and ecosystem knowledge: BigQuery, Composer, Dataproc, Cloud Run, Dataplex
Hands-on experience with orchestration (like Airflow, Dagster, Databricks Workflows) and distributed processing in a cloud environment
Experience with analytical data modelling (star and snowflake schemas), DWH, ETL/ELT patterns, and dimensional concepts
Experience with data governance concepts: access control, retention, data classification, auditability, and compliance standards
Familiarity with CI/CD for data pipelines, IaC (Terraform), and/or DataOps practices
Experience building observability for data systems (metrics, alerting, data quality checks, incident response)

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