Data Engineer (GCP)
Posted Today by Xcede
Negotiable
Outside
Hybrid
Greater London, UK
Summary: As a GCP Data Engineer, you will design, build, and operate scalable data pipelines and infrastructure to ensure high-quality data is accessible for analytics and decision-making. Your role involves collaborating with analysts and data scientists to deliver reliable datasets while implementing best practices for data quality and compliance. You will also optimize workflows for performance and cost efficiency. This position is hybrid, requiring 2-3 days in London, and is classified as outside IR35.
Key Responsibilities:
- 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 (eg, 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
Key Skills:
- 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)
Salary (Rate): undetermined
City: Greater London
Country: UK
Working Arrangements: hybrid
IR35 Status: outside IR35
Seniority Level: undetermined
Industry: IT
GCP Data Engineer
Hybrid 2/3 days - London
OUTSIDE IR/35
As a Data Engineer, you'll 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 you'll 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 (eg, 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)
