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Detailed Description
- Using architecture and data engineering techniques to design and provide tools dedicated to data extraction, analysis and enhancement (build common service layers as much as possible)
- Perform research and analysis (including technological watch) as needed to understand market trends and impact
- Contribute to building & maintaining the global analytic environment of SGL (which includes Data Science & Big data platform, Data Catalog and Data Capture tools) to ease exploitation of data
- Take part in the strategic comity for Data Analytics Solution
- Ensure compliance with policies related to Data Management and Data Protection, in close relationship with the Data Protection Officer, Security & Risk regulation teams
- Contribute to building data engineering pipelines & API for Data Science / Big Data applications
- Take active part in data architecture conception, environments design, core components development based on conceptual architecture/design, etc.
- Design, manage and support PoC, contribute to the choice of tools (build or buy) with all the team & the Group, test solutions. Identify and challenge partners and providers when relevant.
- Document services and build all relevant documentation
- Act as a SME and tech lead / veteran for any data engineering question and manage data engineers within the Data Analytics Solution organization.
- Promote data cultural change within the division to build a data-driven company (convince people of the importance of data, how it should be managed and used, …)
- Collaborate with SGL local teams, FIT department colleagues, IT SME (functional, data, solution and technical architects, data scientists, innovators, business experts…)
- Promote services, contribute to the identification of innovative initiatives within the Group, share information on new technologies in dedicated internal communities.
Job Requirements
- Bachelor’s degree in Statistics, Software Engineering, Engineering, Machine Learning, Mathematics, Computer Science, Economics, or any other related quantitative field.
- Big Data, Analytics or Data Science certification from recognized institutions
- At least 5 years’ experience in BI developments
- Proven and successful experience track record of leading high-performing data engineering teams
- Proven experience on innovation implementation from exploration to production: these may include containerization, Machine learning/AI, Agile environment, on premise and cloud (Azure, AWS, Google) (mandatory).
