Mid Level Site Manager Jobs

Michelin, le leader mondial de la fabrication de pneus embauche!

KEY EXPECTED ACHIEVEMENTS

Data accessibility

• Data Lake (PI & FDW) is implemented in accordance with central specifications.
• Data Lake Access are managed with support of RGQA for D2 Data.
• Is a SPOC (Single point of contact) for all question about data produced by the plant.
• Standard Machine template in PI Osisoft System, related with Data Strategy roadmap, are deployed.
• Identification of functional data requirements and corresponding to different métiers for L2/L4 system development

Data governance/Data quality

• Data governance rules are applied in the local context.
• The quality of data stored into his/her datalake and sent to CDL is guaranteed.
• Non-qualities are detected and corrected (accuracy, completeness, obsolescence and consistency of data, etc.).
• The root causes of data non-quality are identified, analyzed and an action plan is put in place to resolve them.
• Identify and analyze the root causes of data-quality issues and implement corrective action plans.
• Data progress plan and data quality monitoring are animated.
• Data quality control is continually improved.

Networking & collaboration

• The network of data administrators (RUL & Others) is co-animated with the Site Information Manager and best practices are formalized.
• Attend and Contribute to Data Manager network animated by ULD Data Manager.
• Contribution is given on Use case / Digital product deployment.

User support & training

• New users (or new trainers) are trained (train the trainers). Support documentation is available and up to date.
• Users are informed of changes, and application is validated after change application. When needed, degraded working modes are defined as being used during application unavailability.
• Feedback on application and training improvements are collected. Central teams and IM are informed of enhancement needs. When evolutions impact the shop floor, then they are tested and validated.

EXTERNAL CANDIDATE PROFILE

• Bachelor's Degree in Computer Science, Data Science, Information Systems, or an equivalent qualification.
• Minimum 4 years of relevant experience.
• Knowledge of data lakes, data stores, relational databases.
• Ability to query, join, aggregate, validate, and troubleshoot structured data using SQL; familiarity with CSV, JSON, and other common data formats.
• Knowledge on Data pipelines, Databricks, Asure platforms.
• Basic ability to read or develop data-processing scripts using Python.
• Foundational knowledge of cloud services, Power BI, and advanced analytics platforms.
• Define and monitor data-quality rules for accuracy, completeness, consistency, timeliness, and uniqueness.
• Ability to investigate data and application issues, perform root-cause analysis, document corrective actions, and coordinate effectively with Data Engineers, Data Analysts, IS/IT, cybersecurity, and business teams.
• Ability to translate business needs into clear data requirements, document processes and standards, train users, and communicate technical topics to non-technical stakeholders.
• Strong commitment to lifelong learning, problem-solving, customer focus, collaboration, change facilitation, and continuous improvement.
• Passion for innovation, digitalization, sustainable manufacturing, and value creation through trusted data.

Michelin est un employeur qui souscrit au principe de l’égalité des chances en matière d’emploi pour les minorités visibles, les femmes, les autochtones et les personnes handicapées. Si, en raison d'un handicap, vous avez besoin d'un aménagement pour une quelconque partie de la procédure de candidature, veuillez nous contacter à l'adresse suivante: accommodation@michelin.com