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Data Engineer - High Performance Solutions - Job Opening ID: 570418

We are looking for a data engineer to be our data analytics advocate, aiming to foster further data analytics-driven insight, and be an active participant to Saint-Gobain worldwide data analytics community.


In this role, the data engineer supports data analytics efforts related to business intelligence improvement and manufacturing processes transition toward industry 4.0. Working in collaboration with data scientists to scope data analytics projects aiming to support the various businesses of Saint-Gobain. Leads the data engineering efforts of larger scale projects, i.e. she/he designs the data architecture, and carries on the data consolidation, integration, cleaning and structuring.


The responsibilities of the job are summarized below:

  • Integrating, consolidating, cleaning and structuring data for use in analytics applications by data scientists.
  • Conducting meeting with internal customers to identify data analytics needs and scope projects including big data discovery projects and production deployments.
  • Working with data scientists in order to define the requirements in terms of data format and structure for both big data discovery and production mode.
  • For each project, autonomously defining the data architecture, setting up the data pipelines and data processing, and ensuring sustainability of the solution.
  • Collaborating with central IT services and/or external vendors to ensure successful and sustainable developments.
  • Summarizing project approach and results in concise and accurate technical memos.
  • Communicating project strategy and results to internal customers and management through formal presentations.
  • Being an active member of Saint-Gobain worldwide data analytics community, through participation in tech days and sharing of best practices.


  • BS Degree with a minimum of 7+ years’ experience, or MS with 3+ years’ of experience, or entry level Ph.D. in Computer Science, Applied Mathematics or Information Technology.
  • Data Engineering experience with significant business exposure.
  • Enterprise data warehousing.
  • Hadoop and other Big Data frameworks leveraging any one of the Hadoop distributions.
  • Underlying infrastructure (e.g. cloud, Hadoop, NAS, MPP, SAN)



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