Lead Engineer Data Quality & Management

The Opportunity
We are looking for individuals who are excited about our data management and validation and can bring their passion and experience to our growing team. This role will work across Electric machine, Electric Drive Unit (EDU), Inverter, Cell, Battery, Power in loop (PiL) and Vehicle in Loop (ViL) test beds covering. The person in data validation and management role will be responsible to provide timely, accurate, secured and accessible test data for wider engineering group to enable efficient engineering decisions.

• Data Sources and Data Acquisition,Data Quality validation, Data piping, Data storage , Data visualisation
  
  To ensure we can develop world class propulsions systems, we need world class test and data management and validation. The specialist role looks to enable this seeking a person with knowledge of different testing environments data management and validation. The role will require you to work cross functionally to deploy a common approach to data piping, data validation tools as well to build a federated data platform.
  You will be the working towards to become the Subject Matter Expert for data management and validation and deliver a training package to develop others in order to improve the data quality produced by the team.
  
  Key Performance Indicators
  Programme;
  
  
• Full data availability, Data accuracy confidence, Data availability time
  
  Quality Performance;
  
  
• Right First Time (RFT), Data Quality Target, Metrics availability
  
  Delivery Performance;
  
  
• Utilisation and Test Delivery Hours, Schedule Performance Index, DV Delivery Burndown
  
  Key Accountabilities and Responsibilities
  
  
• 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.
  
  
• Undertake any other work as directed by their line manager in connection with their job as may be requested
  
  Knowledge, Skills and Experience
  Essential:
  
  
• Educated to degree level in a natural science, mathematical or engineering or computing discipline.
  
  
• 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.
  
  
• Ability to work closely with engineering stakeholders influence analysis techniques to inform engineering decisions.
  
  
• Develop Key Performance Indicator (KPI) and ability to graphical data presentation.
  
  
• Technical / theoretical understanding of physical measurement equipment
  
  
• Ability to deliver written reports and technical presentations