Data Engineer (GCP)
Posted 6 days ago by 1771246438
Negotiable
Outside
Hybrid
Greater London
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. 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: London
Country: United Kingdom
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)
