Mid/Senior Data Engineer

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/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 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 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 handling complex data problems

Hands-on experience with Apache Spark (PySpark or Spark SQL)

Experience with the Azure data stack

Knowledge of workflow orchestration tools like 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

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 like 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

UKSV (United Kingdom Security Vetting) clearance is required for this role, with Security Check (SC) as the minimum standard, either already held or with a willingness to undergo the process. Some roles/projects may require Developed Vetting (DV) clearance; while not mandatory, a willingness to obtain DV clearance would be beneficial. As part of the onboarding process candidates will be asked to complete a Baseline Personnel Security Standard (BPSS); details of the evidence required to apply may be found on the government website GOV.UK – Government baseline personnel security standard. If you are unable to meet this and any associated criteria, then your employment may be delayed, or rejected. Details of this will be discussed with you at interview.