Looking for Data Management Specialist deployed to Onsite Client OEM: • JD: Be responsible in data sources and data acquisition, data quality validation, data pipeline, data storage and data visualisation for physical test environment. • Delivering data quality reviews in order to support programme targets and prove out chosen technologies. • Develop calculations, parameterisation, regulations and process of in-house data tool development. • Be responsible for confirming the facility is performing in repeatable and reproducible measurements compliant to regulations. • Carry out correlation exercises between test facilities and test beds across test operation. • Ensure continuous improvement of measurement quality by reviewing the measurement equipment, systems and methods used by providing data. • Perform calculation and evaluation of test results, particularly of results from internal correlation measurements, and initiate corrective measures as required. • Cooperate in projects for improving test bed consistency and repeatability (together with experts from internal departments and the Instrumentation & Test Systems business unit). • Working closely with the subject matter expert for Data Quality ensuring that the team KPI’s for Data Quality are met • Recommend and justify process and other improvements to enable improved quality of data • Research, communicate and implement best practice data quality methods and improve test field efficiency. • Investigate best ways of working to make full use of equipment capability in order to improve data quality • To continually encourage a growth mind-set across the team • Coaching and facilitating in best technical practice, knowledge, methods in data validation and management for apprentice, new starter and existing team member development. • Designing applications in different programming languages (preferable Python) • Build data systems & pipelines, data validation and management in cloud platforms (AWS, Google Cloud). • Data analysis skills, including hypothesis testing, uncertainty analysis, and process variation.