Data Engineer

Summary: The role of Data Engineer involves supporting the design, build, and optimization of data platforms and pipelines within a consulting engagement. The position requires collaboration with analysts, engineers, and stakeholders to ensure the delivery of reliable datasets for data-driven decision-making. The contract is outside IR35 and spans an initial period of 3-6 months. The role is remote, available to candidates in the UK or EU.

Key Responsibilities:

• Design, develop, and maintain scalable ELT/ETL pipelines on Google Cloud Platform.
• Model, transform, and test data using dbt; implement best practices for modular, maintainable projects.
• Build and optimise data solutions in BigQuery, including partitioning, clustering, performance tuning, and cost control.
• Orchestrate workflows using Apache Airflow, including scheduling, monitoring, alerting, and retries.
• Develop batch/stream processing solutions using Google Dataflow where appropriate.
• Containerise services with Docker and support deployments in Kubernetes environments.
• Implement data quality checks, lineage, documentation, and CI/CD practices.
• Collaborate with stakeholders to gather requirements and translate them into technical deliverables.

Key Skills:

• Proven experience as a Data Engineer delivering production-grade pipelines and data models.
• Strong hands-on experience with GCP, including BigQuery.
• Solid dbt experience (models, tests, macros, documentation) and SQL expertise.
• Commercial experience with Airflow for orchestration and operational support.
• Working knowledge of Google Dataflow (Apache Beam) for data processing.
• Experience with Docker and Kubernetes in modern data/platform environments.
• Good understanding of data governance, security, and access controls.
• Strong communication skills and the ability to work effectively in a consulting setting.

Salary (Rate): £350 per day

City: undetermined

Country: UK

Working Arrangements: remote

IR35 Status: outside IR35

Seniority Level: undetermined

Industry: IT

Detailed Description From Employer:

Location: UK or EU Remote

Rate: £350 per day/€400 per day

Contract: Outside IR35, 3-6 month initial contract

Overview

We are seeking a Data Engineer to join a consulting engagement, supporting the design, build, and optimisation of robust data platforms and pipelines. You will work closely with analysts, engineers, and stakeholders to deliver reliable, well-governed datasets and enable data-driven decision-making across client environments.

Key Responsibilities

• Design, develop, and maintain scalable ELT/ETL pipelines on Google Cloud Platform.
• Model, transform, and test data using dbt; implement best practices for modular, maintainable projects.
• Build and optimise data solutions in BigQuery, including partitioning, clustering, performance tuning, and cost control.
• Orchestrate workflows using Apache Airflow, including scheduling, monitoring, alerting, and retries.
• Develop batch/stream processing solutions using Google Dataflow where appropriate.
• Containerise services with Docker and support deployments in Kubernetes environments.
• Implement data quality checks, lineage, documentation, and CI/CD practices.
• Collaborate with stakeholders to gather requirements and translate them into technical deliverables.

Essential Requirements

• Proven experience as a Data Engineer delivering production-grade pipelines and data models.
• Strong hands-on experience with GCP, including BigQuery.
• Solid dbt experience (models, tests, macros, documentation) and SQL expertise.
• Commercial experience with Airflow for orchestration and operational support.
• Working knowledge of Google Dataflow (Apache Beam) for data processing.
• Experience with Docker and Kubernetes in modern data/platform environments.
• Good understanding of data governance, security, and access controls.
• Strong communication skills and the ability to work effectively in a consulting setting.

Desirable

• 1 year experience working with mobile data would be beneficial.
• Experience integrating multiple data sources (APIs, event data, relational and semi-structured formats).