Global IS Data

Ready to take on meaningful challenges and shape the next generation of mobility?

Joining OPmobility means stepping into a global group with a purpose to keep people and goods moving safely, smarter, and sustainably everywhere.

Context

You are joining the Digital & IS Team in OPmobility headquarters in Levallois-Perret. You will report to the Group Enterprise Architect.

Activities

Operational

• Lead global & collaborative modelling of Opmobility common internal conceptual Business Object Model in UML. This model is to be done in close collaboration with data managers defining business terms and with domain architects assisting with the modelling for their specific domain and within their projects.

Global IS AI Data & Integration Architecture must ensure:

• the completeness & coherency of the model,
• agreement of different stakeholders working on the same data,
• definition & respect of best practices (eg: modelling tool best practices usage, ensuring the link between BOM and logical data models in applications, API signatures),
• usability of the model for APIs signatures.
• appropriate presentations and awareness for the whole team working on or using models incl. explanation and links of different kind of data models.
• In the absence of architects or data managers, the global AI Data & Integration architect should be able to ensure the modelling himself.

Strategic & Foundational

• Assist the development of required tooling, possible RFPs and suggest missing data architecture capabilities & practices

Operational

• Lead the global modelling of data architecture dissemination and lifecycle through OPmobility in collaboration with data managers defining business terms, BPOs knowing the data lifecycle and with domain architects assisting the modelling for their specific domain and within their projects
• Ensure complete data flows are described for each AI application and where required by AI act legislation in collaboration with data teams, AI business owners and associated application responsibilitles
• Assist domain architects while deciding the best data sources for their project (golden sources, trusted sources, MDM)

Strategic & Foundational

• Assist the development of required tooling, possible RFPs and suggest missing data architecture capabilities & practices
• Ensure synthetic presentation, communication and awareness des golden sources

Integration

Operational

• Be the point of contact for all projects to assist the choice of best protocol/technology while thriving for best compromise of reusable interfaces, time to market improvements, costs optimization and respect of the common language
• Ensure the reusability of APIs, basing them on standard common language and enabling thus the best AI data quality

Strategic & Foundational

• Define good practices of integration patterns, specifications in collaboration with Integration practice, cyber security and platform technical architects
• Ensure communication & awareness of the above practices for all project teams

Assist the development of required tooling, possible RFPs and suggest missing data integration capabilities & practices

Profile :

• Master's degree (Bac+5) in Engineering or Computer Science.
• DAMA certification is plus
• Minimum 10 years of experience with :
• at least 5 years of data modelling in different business and projects roles contexts (conceptual modelling of data business requirements, logical modelling of databases, reusable API signatures designs).
• at least 4 years of experience in integration practice
• experiencing different transfer protocols/technologies (ETL, API, Webservice, files transfer, event management, EDIs, Iot exchanges, SAP replications)
• assisting application delivery teams to choose the appropriate protocol/technology depending on project context with definition of pro/cons for each scenario incl. TCO, data load & freshness, delays
• defining standards transfer patterns, documentations and facilitating work of development teams
• Sound knowledge of data architecture (good practices of data dissemination and distribution over applications, golden sources, trusted source concepts) with ability to model CRUDs and data lineage and lifecycle
• Good understanding of different type of data: master, transactional, reference data, unstructured data
• Good understanding of Data Governance concepts and processes incl Data quality criteria
• Knowledge of enterprise architecture concepts (capabilities, processes, applications, flows)
• Knowledge of data base technologies, SAP, MES, SaaS/IaaS, IoT & plants local Edge architectures.
• Goo