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 transformationWorking on client data foundation engagements that combine discovery, stabilisation and remediationEstablish reusable engineering standards, patterns and documentation that support quality, maintainability and repeatable Data Foundations delivery across future engagementsEstablish technical standards and patterns that ensure quality and maintainabilityHelp cultivate a data-driven culture within the organisationEnable business leaders to make informed decisions with confidence through timely, accurate data insightsElevate the technical capabilities of the entire data engineering teamDeliver seamless data solutions that enhance user experienceDrive adoption of modern data architectures and platformsAdvanced knowledge of optimisation techniques for large-scale data processingHands-on experience with Apache Spark (PySpark or Spark SQL)Knowledge of workflow orchestration tools like Azure Data Factory or Apache AirflowDeep understanding of data warehouse design principles and methodologiesExperience with the Azure data stackStrong proficiency in SQL and Python for handling complex data problemsUnderstanding 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 modelHands-on experience profiling data quality issues, defining cleansing rules, mapping data between systems, validating reconciliation outputs and documenting exceptions for business reviewExperience implementing and advocating for test-driven development methodologies in data pipeline workflows, including unit testing, integration testing, and data quality validation frameworksStrong data architecture and modelling skills with the ability to design scalable data solutionsExperience with containerisation technologies like DockerProficiency in dimensional modelling techniquesProven experience leading technical aspects of data projectsExperience with CI/CD pipelines for data solutionsExperience working with data from Ariba, Workday, SAP S/4HANA or comparable procurement, workforce, timesheet, finance, supplier invoice or locally maintained spreadsheet sourcesAbility 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 fixesStrong communication skills for translating complex technical conceptsExperience designing and implementing data mesh or data fabric architecturesKnowledge of cost optimisation strategies for cloud data platformsExperience with data quality frameworks and implementationExperience with data visualisation tools like Power BI or Apache SupersetExperience with other cloud data platforms like AWS, GCP or OracleExperience with modern unified data platforms like Databricks or Microsoft FabricExperience with Kubernetes for container orchestrationUnderstanding of streaming technologies (Apache Kafka, event-based architectures)Experience with high-performance, large-scale data systems