Mid​/Senior Data Engineer

Methods is recruiting for a permanent Mid/Senior Data Engineer to join the Data and AI Capability Centre. This role will be mainly remote but require flexibility to travel to client sites, and our offices based in London, Sheffield, and Bristol.

You will support complex client engagements where data engineering is used to stabilise business-critical processes, improve reporting confidence and establish repeatable data foundations across enterprise systems. Bringing 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 should be comfortable working 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. Working on client data foundation engagements that combine discovery, stabilisation and remediation.

Typical work will include understanding process and system landscapes, identifying data and reconciliation issues, supporting tactical fixes, and helping clients define the data architecture, reporting and governance foundations needed for longer‑term transformation.

• Design, build and improve ETL and ELT pipelines that support data ingestion, profiling, reconciliation, cleansing and reporting across enterprise source systems.
• Building data catalogues, data flows, interface views and trusted source views
• Design and architect modern data solutions that align with business objectives and technical requirements, supporting current‑state and target‑state data architecture
• Help clients improve confidence in operational, workforce, procurement and financial reporting through timely, accurate and reconcilable data.
• Build highly scalable and performant data solutions leveraging cloud platforms and open‑source software
• Develop data models to handle enterprise‑level analytical needs
• Optimise large‑scale data processing systems for performance and cost‑efficiency
• Implement robust data quality frameworks and monitoring solutions
• Evaluate new technologies to enhance our data engineering capabilities
• Collaborate with stakeholders to translate business requirements into technical specifications
• Present technical solutions to leadership and non‑technical stakeholders
• Contribute to the development of the Methods Analytics Engineering Practice by participating in our internal community of practice

• Enable business leaders to make informed decisions with confidence through timely, accurate data insights
• Establish reusable engineering standards, patterns and documentation that support quality, maintainability and repeatable Data Foundations delivery across future engagements.
• Drive adoption of modern data architectures and platforms
• Deliver seamless data solutions that enhance user experience
• Elevate the technical capabilities of the entire data engineering team
• Help cultivate a data‑driven culture within the organisation
• Establish technical standards and patterns that ensure quality and maintainability

• Experience working with data from Ariba, Workday, SAP S/4
  
  HANA 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 turn 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 documentation that can be reused as part of an enduring data governance model.
• Experience implementing and advocating for test‑driven development methodologies in data pipeline workflows, including unit testing, integration…