Specialists who design, build, and maintain the data warehouses that power business intelligence and analytics. A critical role in the data engineering ecosystem, ensuring data is accessible and reliable.
Data Warehouse Engineers are the architects and custodians of the data infrastructure that underpins modern business intelligence. They design, build, and maintain data warehouses, ensuring that data is stored efficiently, securely, and in a format that can be easily queried and analysed. These engineers work closely with data scientists, BI analysts, and other stakeholders to ensure that the data infrastructure meets the organisation's needs. Common employers include tech scaleups, financial institutions, and large enterprises with complex data requirements.
A typical week for a Data Warehouse Engineer is split between designing and implementing new data pipelines, maintaining existing infrastructure, and collaborating with cross-functional teams.
% of 1 listings posted in the last 12 months that mention each skill, extracted from job descriptions.
A typical UK progression for data warehouse engineers. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.
Assists in the design and implementation of data pipelines and ETL processes. Focuses on learning and contributing to small projects.
Takes ownership of specific data pipelines and ETL processes. Collaborates with cross-functional teams to meet data requirements.
Leads the design and implementation of complex data infrastructure projects. Mentors junior engineers and ensures best practices are followed.
Strategic leader in data engineering, responsible for the overall architecture and direction of the data warehouse. Advises on data strategy and innovation.
There's no single route, but most people follow some version of these steps.
Start with foundational knowledge in SQL, data modelling, and ETL processes. Gain hands-on experience with tools like Apache Airflow and Snowflake.
Work on real-world projects to design and implement data pipelines. Collaborate with data scientists and BI teams to understand data requirements.
Focus on a specific area such as financial data, healthcare data, or e-commerce data. Develop deep expertise in that domain.
Take on leadership roles in data engineering projects. Manage teams and ensure the successful delivery of data infrastructure solutions.
Transition into a strategic role, advising on data architecture and innovation. Influence the direction of the organisation's data strategy.
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As a Senior AI Engineer at PoliceAI, you will lead the design, development, and deployment of AI-enabled products, translating policing needs into technical solutions. You will work with multidisciplinary teams to build secure, maintainable, and scalable services, and support the testing, evaluation, and validation of AI products.
This role involves developing and supporting enterprise data pipelines and OpenShift-based solutions, collaborating with cross-functional teams to deliver cloud-native applications, and enhancing platform performance. The developer will work on scalable, secure, and efficient data processing and operational transformation initiatives using Scala, Apache Spark, and OpenShift.
This role involves leading the technical architecture for an Azure platform, designing and building large-scale data pipelines, and integrating end-to-end data solutions. The focus is on cloud migration, data ingestion, and ensuring data quality and consistency.
This role involves leading the migration of IBM DB2 databases to AWS Aurora PostgreSQL, designing and implementing data models, and communicating complex data concepts to stakeholders. You will work 2-3 days per week onsite in Farnborough, collaborating with business and technology teams to deliver data architecture solutions.
You will design, develop, and support data integration, warehousing, and reporting solutions for a secure government programme. The role involves working with Oracle databases, ETL processes, and data quality, within a complex enterprise environment.
This role involves designing and implementing complex data flows, building robust data models, and developing cloud-based data infrastructure using GCP, BigQuery, Dataform, and Airflow. You'll work closely with developers, analysts, and other data professionals to ensure strong data quality and governance.
Common tools include SQL, Apache Airflow, Snowflake, Amazon Redshift, and Google BigQuery. Familiarity with Python and data modelling software is also beneficial.
A Data Warehouse Engineer focuses specifically on designing and maintaining data warehouses, while a Data Engineer may have a broader scope, including data pipelines and data lakes.
Gain experience in designing and implementing data pipelines, collaborate with cross-functional teams, and continuously improve your skills in data modelling and ETL processes.
Salary ranges can vary widely based on experience, location, and industry. For more detailed information, refer to the salary section on this page.
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