Data Audit Project Manager

For our client, a global consultancy company, with a strong focus on digital learning and educational innovation, we are looking for a Data Audit Project Manager_Automotive industry.

Location: Prague/Mlada Boleslav and surroundings (CZECH REPUBLIC)

Work model: hybrid

Type of collaboration: CoE

Mandatory languages: Czech and English

The Mission:

• Thematically, it involves working with data during the early stages of car development in the Cockpit and Vorderwagen (front part of the car) areas.
• The subject of the audit (analysis) is the design evaluation process (storage mapping, data and information exchange/sharing, availability).
• The aim of the audit is to analyse and describe the "space" for standardization and increasing the efficiency and quality of data and information using/sharing.
• It is necessary to reflect the change from physical car development to virtual.
• As part of work activities on car projects, a significant amount of data is generated, which is stored in directory systems and further shared/re-stored and modified (versioned) as needed.
• The system, processes, and methodologies for storing/accessing data and for their reuse are not unified across individual workgroups.
• Internal processes and methodologies for working with data are either outdated (do not reflect current possibilities of new technologies and project requirements) or incomplete. This is manifested, for example, by the fact that in some cases, workgroups do duplicate work instead of reusing/building on previous outputs.

Responsibilities:

• Conduct the data audit, analyze the current state, processes, tools, and work environment.
• Identify and describe inefficiencies and problems with data collection/storage and utilization.
• Identify root causes (processes, data availability, data access, etc.) and propose action steps for improvement.
• Propose a transformation path to increase the quality and efficiency of working with data and information (data-driven company), the goal being to guide the department through this transformation and transfer knowledge to internal users.
• Train the representatives during the project.
• Timely delivery of use cases.
• Propose a solution – organization of data and information and setting/standardizing workflows/processes for effective project management, considering the use of advanced data analytics and AI technologies.
• Detailed project plan including a schedule.
• Transfer of know-how to client's employees.

Relevant Experience & Industry Background:

• Experience in the automotive industry, preferably in vehicle development, engineering, or data management roles, with a proven track record in data auditing, process optimization, and/or digital transformation projects.
• Experience working in early-stage vehicle development, especially in Cockpit (interior systems) and Vorderwagen (front-end structure) domains.
• Familiarity with ESA (Elektrik/Elektronik System Architektur) or similar departments focused on electrical/electronic systems architecture.

Technical Skills & Tool Familiarity:

• Understanding of PLM systems (e.g., Siemens Teamcenter, Dassault ENOVIA), and CAD platforms (e.g., CATIA, NX) with their data workflows.
• Knowledge of data lakes, data warehouses, and ETL pipelines.
• Familiarity with version control systems (e.g., Git, SVN)
• Exposure to AI/data analytics tools (e.g., Python, Power BI, Tableau, Jupyter, SQL) for data analysis and visualization.
• Understanding of digital twin concepts and simulation environments used in virtual car development.

Knowledge of Virtual Car Development Workflows:

• Insight into model-based systems engineering (MBSE), digital engineering practices, virtual validation, simulation data management, and cross-functional collaboration in early-stage development.
• Understanding of data flow between design, simulation, testing, and manufacturing phases.

Soft Skills for Stakeholder Engagement & Knowledge Transfer:

• Excellent communication and presentation skills to engage with engineers, managers, and IT stakeholders.
• Strong analytical thinking and problem-solving abilities.
• Ability to translate technical findings into actionable business recommendations.
• Skilled in change management, training, and mentoring internal teams.

Educational Background:

• Bachelor's or Master's degree in Mechanical Engineering, Automotive Engineering, Data Science, Information Systems, or related fields.