Mid/Senior Data Engineer

• Methods is recruiting for a permanent Mid/Senior Data Engineer to join the Data and AI Capability Centre
• 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
• 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

Benefits

• Autonomy to develop and grow your skills and experience
• Be part of exciting project work that is making a difference in society
• Strong, inspiring and thought-provoking leadership
• A supportive and collaborative environment
• Development access to LinkedIn Learning, a management development programme and training
• Wellness 24/7 Confidential employee assistance programme
• Social - Breakfast Tuesdays, Pizza Thursday monthly, weekly social in the office and commitment to charitable causes
• Time off 25 days a year
• Pension Salary Exchange Scheme with 4% employer contribution and 5% employee contribution
• Discretionary Company Bonus based on company and individual performance
• Life Assurance of 4 times base salary
• Private Medical Insurance which is non-contributory (spouse and dependants included)
• Worldwide Travel Insurance which is non-contributory (spouse and dependants included)
• Benefits Platform offering various retail and leisure discounts

• 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
• Establish technical standards and patterns that ensure quality and maintainability
• Enable business leaders to make informed decisions with confidence through timely, accurate data insights
• Elevate the technical capabilities of the entire data engineering team
• Establish reusable engineering standards, patterns and documentation that support quality, maintainability and repeatable Data Foundations delivery across future engagements
• Help cultivate a data-driven culture within the organisation
• Drive adoption of modern data architectures and platforms
• Deliver seamless data solutions that enhance user experience
• Strong data architecture and modelling skills with the ability to design scalable data solutions
• Experience with containerisation technologies like Docker
• Hands-on experience profiling data quality issues, defining cleansing rules, mapping data between systems, validating reconciliation outputs and documenting exceptions for business review
• Experience with the Azure data stack
• Experience working with data from Ariba, Workday, SAP S/4HANA or comparable procurement, workforce, timesheet, finance, supplier invoice or locally maintained spreadsheet sources
• Experience implementing and advocating for test-driven development methodologies in data pipeline workflows, including unit testing, integration testing, and data quality validation frameworks
• Advanced knowledge of optimisation techniques for large-scale data processing
• Proven experience leading technical aspects of data projects
• Experience with CI/CD pipelines for data solutions
• Strong communication skills for translating complex technical concepts
• Deep understanding of data warehouse design principles and methodologies
• Strong proficiency in SQL and Python for handling complex data problems
• Knowledge of workflow orchestration tools like Azure Data Factory or Apache Airflow
• Proficiency in dimensional modelling techniques
• 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
• Hands-on experience with Apache Spark (PySpark or Spark SQL)
• 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 designing and implementing data mesh or data fabric architectures
• Experience with data quality frameworks and implementation
• Knowledge of cost optimisation strategies for cloud data platforms
• 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