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 38 UK listings priced in pounds in the last 12 months. Base salary only.
% of 39 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.
Head Resourcing Falkirk £60,000 – £70,000 pa
Leads data modelling, transformation and governance to turn data into trusted insights for operational and commercial decisions. Builds scalable data solutions using SQL, dbt, Power BI semantic models and a modern cloud platform such as Databricks, while managing stakeholder and vendor relationships to support a five-year data plan.
Data Idols London £400 – £550 pd
Analytics Engineer - ContractDay Rate: £400 - £550 per dayLocation: London | RemoteData Idols are working with a growing, product-led technology business that is continuing to invest in its data and analytics capabilities. They are looking for an experienced Analytics...
Harnham - Data and Analytics Recruitment London £50,000 – £60,000 pa
Builds and maintains scalable data pipelines using Azure Data Factory and Databricks, integrating data from finance, HR and operational systems. Develops a lakehouse architecture with Kimball and star-schema models, and supports the Power BI reporting layer including semantic models and DAX. Joins an early-stage data function, shaping standards and working closely with business stakeholders.
Synapri Sheffield £65,000 – £75,000 pa
Owns the curated data layer within a Databricks Lakehouse, designing dimensional and semantic models and building complex SQL and PySpark transformation pipelines. Defines KPI logic, conformed dimensions and data quality standards, leads modelling design reviews and mentors engineers while working with architects, BI developers and business stakeholders. Remote with ad-hoc client site travel.
Intec Select London £650 – £750 pd
Lead the migration of log sources from Splunk ingestion to Cribl Stream/Edge, designing load-balanced, fault-tolerant pipelines for high ingest volumes. Onboard and tune security log sources for SOC and IR teams, build Splunk and Cribl configs, and monitor pipeline health while reporting on ingest reduction and cost savings.
Harnham - Data and Analytics Recruitment London £70,000 – £85,000 pa
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.
Source United Kingdom £44,000 pa
Develop analytical outputs, reports and statistical publications, and build dashboards and visualisations that support evidence-based decisions. Analyse customer, market and operational data, automate reporting processes and strengthen data quality controls, while exploring AI and emerging technologies. Acts as deputy to the Senior Data Analyst and works with stakeholders across the organisation.
Corriculo Reading £70,000 – £80,000 pa
Owns a broad analytics remit covering data warehouse development, pipeline reliability, BI and customer insight. Builds transformation models, defines company metrics, creates dashboards, and troubleshoots data quality issues while partnering with Sales, Marketing and Operations. Works with SQL, Python, Snowflake and dbt in a growing healthtech team.
The locations with the most live listings for this role today.
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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