Location
Primarily remote with flexibility to travel to client sites and offices in London, Sheffield, and Bristol.

Hours
Full Time

Salary
Competitive salary offered

About the Role
Methods Business and Digital Technology is seeking a permanent Mid/Senior Data Engineer to join the Data and AI Capability Centre. In this role, you will support complex client engagements focused on stabilising business-critical processes, improving reporting confidence, and establishing repeatable data foundations across enterprise systems. You will bring strong hands-on experience in data profiling, cleansing, mapping, reconciliation, and integration across complex business systems, ideally with exposure to procurement, workforce, finance, ERP, or source-to-pay data.

You will work iteratively with architects, process owners, and business stakeholders to identify root causes, support tactical fixes, improve reporting confidence, and help establish repeatable data foundations for future transformation. Typical responsibilities include understanding process and system landscapes, identifying data and reconciliation issues, supporting tactical fixes, and helping clients define data architecture, reporting, and governance foundations needed for longer-term transformation.

Key activities include designing, building, and improving ETL and ELT pipelines for data ingestion, profiling, reconciliation, cleansing, and reporting across enterprise source systems. You will build data catalogues, data flows, interface views, and trusted source views, and design modern data solutions aligned with business objectives and technical requirements. You will help clients improve confidence in operational, workforce, procurement, and financial reporting through timely, accurate, and reconcilable data.

The role involves building highly scalable and performant data solutions leveraging cloud platforms and open-source software, developing data models for enterprise-level analytical needs, optimising large-scale data processing systems for performance and cost-efficiency, and implementing robust data quality frameworks and monitoring solutions. You will evaluate new technologies, collaborate with stakeholders to translate business requirements into technical specifications, present technical solutions to leadership and non-technical stakeholders, and contribute to the development of the Methods Analytics Engineering Practice by participating in the internal community of practice.

Your impact will include enabling business leaders to make informed decisions with confidence through timely, accurate data insights, establishing reusable engineering standards, patterns, and documentation to support quality and maintainability, driving adoption of modern data architectures and platforms, delivering seamless data solutions that enhance user experience, elevating the technical capabilities of the data engineering team, cultivating a data-driven culture, and establishing technical standards that ensure quality and maintainability.

Experience
- Working with data from Ariba, Workday, SAP S/4HANA, or comparable procurement, workforce, timesheet, finance, supplier invoice, or locally maintained spreadsheet sources.
- Hands-on experience profiling data quality issues, defining cleansing rules, mapping data between systems, validating reconciliation outputs, and documenting exceptions for business review.
- Ability to work iteratively with architects, process owners, finance, procurement, workforce, and operational stakeholders to translate ambiguous business issues into clear data analysis, engineering actions, and controlled tactical fixes.
- Understanding of data ownership, stewardship, lineage, metadata, controls, and data quality monitoring, with the ability to produce reusable documentation as part of an enduring data governance model.
- Experience implementing and advocating test-driven development methodologies in data pipeline workflows, including unit testing, integration testing, and data quality validation frameworks.
- Proven experience leading technical aspects of data projects.
- Strong data architecture and modelling skills with the ability to design scalable data solutions.
- Deep understanding of data warehouse design principles and methodologies.
- Advanced knowledge of optimisation techniques for large-scale data processing.
- Strong proficiency in SQL and Python for complex data problems.
- Hands-on experience with Apache Spark (PySpark or Spark SQL).
- Experience with the Azure data stack.
- Knowledge of workflow orchestration tools such as Azure Data Factory or Apache Airflow.
- Experience with containerisation technologies like Docker.
- Proficiency in dimensional modelling techniques.
- Experience with CI/CD pipelines for data solutions.
- Strong communication skills for translating complex technical concepts.

Desirable Skills
- Experience designing and implementing data mesh or data fabric architectures.
- Knowledge of cost optimisation strategies for cloud data platforms.
- Experience with data quality frameworks and implementation.
- Experience with data visualisation tools like Power BI or Apache Superset.
- Experience with other cloud data platforms such as AWS, GCP, or Oracle.
- Experience with modern unified data platforms like Databricks or Microsoft Fabric.
- Experience with Kubernetes for container orchestration.
- Understanding of streaming technologies (Apache Kafka, event-based architectures).
- Experience with high-performance, large-scale data systems.

Security Clearance
UKSV (United Kingdom Security Vetting) clearance is required, with Security Check (SC) as the minimum standard, either already held or willingness to undergo the process. Some roles/projects may require Developed Vetting (DV) clearance; while not mandatory, willingness to obtain DV clearance is beneficial. Candidates will be asked to complete a Baseline Personnel Security Standard (BPSS) as part of onboarding. Inability to meet these criteria may delay or prevent employment; details will be discussed at interview.