Specialists who design, build, and maintain the data pipelines that power business intelligence. A critical role in transforming raw data into actionable insights.
Analytics Engineers are the backbone of modern data infrastructure, focusing on the design, implementation, and maintenance of data pipelines and warehouses. They work closely with data scientists, business analysts, and other stakeholders to ensure that data is clean, reliable, and accessible. In the UK, this role is in high demand, particularly in sectors like finance, e-commerce, and tech, where data-driven decision-making is crucial.
A typical week for an Analytics Engineer is a mix of coding, testing, and collaboration. They spend time writing SQL and Python scripts, optimising data flows, and working with cross-functional teams.
Based on advertised midpoints across the 32 UK listings priced in pounds in the last 12 months. Base salary only.
% of 23 listings posted in the last 12 months that mention each skill, extracted from job descriptions.
A typical UK progression for analytics engineers. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.
Assist in the design and implementation of data pipelines, focusing on learning and contributing to small projects.
Take ownership of specific data pipelines and work on more complex projects, often leading small teams.
Lead the design and implementation of large-scale data infrastructure, mentor junior engineers, and drive best practices.
Strategise and oversee the entire data engineering function, influence company-wide data architecture, and lead cross-functional initiatives.
There's no single route, but most people follow some version of these steps.
Start with foundational skills in SQL, Python, and data warehousing. Gain experience with ETL/ELT tools and data modelling.
Work on small to medium-sized data pipelines, focusing on data extraction, transformation, and loading processes.
Refine your skills in data optimisation and scalability. Learn to handle large datasets and improve data processing efficiency.
Take on leadership roles in data engineering projects. Manage teams and ensure the successful delivery of complex data solutions.
Contribute to the strategic direction of the data engineering function. Influence company-wide data architecture and best practices.
Mentor junior engineers and drive innovation in data engineering. Stay ahead of industry trends and continuously improve data infrastructure.
This role involves designing and building analytics-ready data models, enabling self-service reporting, and delivering trusted datasets to support strategic business decisions. You will work closely with stakeholders to translate business requirements into robust data solutions, using SQL and cloud data platforms like GCP/BigQuery.
As an Analytics Engineer, you will develop and maintain data pipelines using Python, design dimensional data models in a cloud data platform like Snowflake, and partner with BI teams to deliver actionable insights. You'll work closely with cross-functional teams to drive data-led decision making and optimize the performance and reliability of the data platform.
This role involves building and maintaining data pipelines using Python, designing dimensional data models in Snowflake, and delivering high-quality datasets for Power BI reporting. You will work closely with business stakeholders to translate requirements into scalable data solutions and optimize the data platform's performance and cost efficiency.
This role involves designing and maintaining scalable data pipelines, integrating data from various business systems, and developing robust data models. You will work closely with stakeholders to translate requirements into usable data assets and support the growth of the data function.
The Analytics Engineer will design and maintain Power BI reports, support the implementation of Snowflake, and manipulate large datasets to provide actionable insights. They will collaborate with cross-functional teams to understand and meet their analytical needs, using SQL and Python to enhance data processing and reporting.
The Energy Analytics Engineer will design and maintain data pipelines using Azure-based tools, automate data ingestion from APIs, and support reporting and analytics for energy and sustainability initiatives. The role involves close collaboration with cross-functional teams to deliver robust data models, ensure data quality, and enable data-driven decision-making. A key focus is on enabling carbon reporting and driving digital solutions that support Veolia’s ecological transformation goals.
The Senior Analytics Engineer will design and maintain cloud-based data platforms using Azure technologies, focusing on ETL/ELT workflows, data warehousing, and analytics solutions. This role involves close collaboration with business and technical teams to translate requirements into scalable data architectures, ensure data quality, and support regulatory compliance. The position requires strong expertise in SQL, Azure Data Factory, Fabric, and CI/CD automation, with an emphasis on documentation, performance tuning, and secure data practices.
This role involves designing and maintaining data transformations, building scalable data products, and enabling reliable analytics through close collaboration with analysts and business stakeholders. The engineer will work extensively with SQL, cloud data platforms, and dbt to create trusted datasets that power business intelligence and decision-making. Emphasis is placed on best practices in analytics engineering, data modelling, and automation within a mature data environment.
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Common tools include SQL, Python, dbt, Apache Airflow, and various data warehousing solutions like Snowflake and BigQuery.
Analytics Engineers provide the data infrastructure that data scientists rely on. They collaborate to ensure data is clean, reliable, and accessible for analysis.
Key skills include strong SQL and Python programming, data modelling, ETL/ELT processes, and a deep understanding of data warehousing and data lakes.
The typical progression is from Junior to Mid-Level, Senior, and finally Principal Analytics Engineer, with increasing responsibility and leadership.
Salaries vary based on experience and location. For more detailed salary information, please refer to the salary section on this page.
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