At Danone, we believe that every time we eat or drink, we vote for the world we want. As a leading food and beverage company, we make healthy, sustainably produced foods and beverages that nourish people’s lives. As the largest Certified B Corporation® in the world, we are committed to redefining success in business to ensure that through our brands we create real benefits for people, our communities, and the planet.

In IT & Data, we inspire and empower Danone through the world of tech and data. Our mission is that our technology and data are strategic differentiators of Danone, powered by an agile IT & Data organisation.

As Zone Data Engineering Lead, you will create a cohesive and collaborative environment where data engineers can share knowledge, standardize practices, and drive innovation. By focusing on these areas, the Zone Lead Data Engineer helps ensure that data engineering efforts across the organization are aligned, efficient, and effective, ultimately contributing to better data-driven decision-making and business outcomes.

Key accountabilities, deliverables & KPIs, impact on business results

1. Accountable for knowledge sharing and collaboration:
   • Facilitate regular workshops, and discussions to share knowledge, tools, techniques, and best practices among data engineers.
   • Encourage collaboration on common challenges and innovations, fostering a community of practice.
2. Accountable for establishing standards and best Practices:
   • Define and document best practices and coding standards,
   • Ensure consistency in data engineering practices across squads or teams to maintain data quality and integrity.
3. Accountable for providing technical guidance and support:
   • Provide technical guidance and mentorship to data engineers, helping them solve complex problems and stay updated with the latest industry trends and technologies.
   • Offer support for critical capability issues and ensure best practices in data pipeline development, data warehousing, and data integration.
4. Responsible for tooling and technology Stack:
   • Evaluate and recommend tools and technologies that the organization should adopt for data engineering.
   • Standardize the technology stack used for data engineering tasks to streamline operations and reduce redundancy.
5. Responsible for internals development & externals evaluation:
   • Organize training sessions, certifications, and professional development opportunities to help data engineers enhance their skills and grow in their careers.
   • Promote a culture of continuous learning and improvement within the chapter.
   • Encourage experimentation with new techniques, and approaches to improve data engineering processes and outcomes.
   • Foster an environment where data engineers can innovate and contribute to the organization's data strategy.
6. Contribute to cross-functional collaboration:
   • Work closely with other chapters, such as those focused on data science, platform engineering, or data visualization, to ensure alignment and integration of data initiatives.