The ad says“Knowledge of workflow orchestration tools like Azure Data Factory or Apache Airflow”
The ad says“Understanding of streaming technologies (Apache Kafka, event-based architectures)”
The ad says“Hands-on experience with Apache Spark (PySpark or Spark SQL)”
The ad says“Experience with data visualisation tools like Power BI or Apache Superset”
The ad says“Experience working with data from Ariba, Workday, SAP S/4HANA or comparable procurement, workforce, timesheet, finance, supplier invoice or locally maintained spreadsheet sources”
The ad says“Experience with other cloud data platforms like AWS, GCP or Oracle”
The ad says“Knowledge of workflow orchestration tools like Azure Data Factory or Apache Airflow”
The ad says“Experience with the Azure data stack”
The ad says“Experience with CI/CD pipelines for data solutions”
The ad says“Design and architect modern data solutions that align with business objectives and technical requirements, supporting current-state and target-state data architecture”
The ad says“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”
The ad says“Experience designing and implementing data mesh or data fabric architectures”
The ad says“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”
The ad says“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”
The ad says“Experience designing and implementing data mesh or data fabric architectures”
The ad says“Strong data architecture and modelling skills with the ability to design scalable data solutions”
The ad says“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”
The ad says“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”
The ad says“Experience with modern unified data platforms like Databricks or Microsoft Fabric”
The ad says“Proficiency in dimensional modelling techniques”
The ad says“Experience with containerisation technologies like Docker”
The ad says“Design, build and improve ETL and ELT pipelines that support data ingestion, profiling, reconciliation, cleansing and reporting across enterprise source systems”
The ad says“Design, build and improve ETL and ELT pipelines that support data ingestion, profiling, reconciliation, cleansing and reporting across enterprise source systems”
The ad says“Experience with other cloud data platforms like AWS, GCP or Oracle”
The ad says“Experience with Kubernetes for container orchestration”
The ad says“Experience with modern unified data platforms like Databricks or Microsoft Fabric”
The ad says“Experience with other cloud data platforms like AWS, GCP or Oracle”
The ad says“Experience with data visualisation tools like Power BI or Apache Superset”
The ad says“Hands-on experience with Apache Spark (PySpark or Spark SQL)”
The ad says“Strong proficiency in SQL and Python for handling complex data problems”
The ad says“Experience working with data from Ariba, Workday, SAP S/4HANA or comparable procurement, workforce, timesheet, finance, supplier invoice or locally maintained spreadsheet sources”
The ad says“Hands-on experience with Apache Spark (PySpark or Spark SQL)”
The ad says“Strong proficiency in SQL and Python for handling complex data problems”
The ad says“Experience implementing and advocating for test-driven development methodologies in data pipeline workflows, including unit testing, integration testing, and data quality validation frameworks”
The ad says“Experience working with data from Ariba, Workday, SAP S/4HANA or comparable procurement, workforce, timesheet, finance, supplier invoice or locally maintained spreadsheet sources”
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
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
Preferred experience
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
