Digital Data Engineer

We are an innovative global healthcare company, driven by one purpose: we chase the miracles of science to improve people’s lives. Our team, across some 100 countries, is dedicated to transforming the practice of medicine by working to turn the impossible into the possible. We provide potentially life-changing treatment options and life-saving vaccine protection to millions of people globally, while putting sustainability and social responsibility at the center of our ambitions.

Sanofi has recently embarked into a vast and ambitious digital transformation program. A cornerstone of this roadmap is the acceleration of its data transformation and of the adoption of artificial intelligence (AI) and machine learning (ML) solutions that will accelerate Manufacturing & Supply performance and help bring drugs and vaccines to patients faster, to improve health and save lives.

• Developing and maintaining data warehouse solutions for storage and retrieval of structured and unstructured data.
• Building and optimizing Extract, Transform, Load (ETL) processes to integrate large datasets from various sources into data systems.
• Implementing real-time data streaming and processing solutions to support business analytics and operational needs.
• Creating schemas that support data analytics and reporting requirements.
• Ensuring data integrity, quality, and compliance with organizational standards and regulatory requirements.
• Integrating data from multiple sources and formats (batch, streaming) into cohesive datasets for analysis.
• Transforming raw data into a structured format suitable for analytics and reporting purposes.
• Proficiency in SQL databases (e.g., Snowflake, PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra).
• Experience with distributed computing frameworks (e.g., Hadoop, Spark) and cloud-based data platforms (e.g., AWS, Azure, GCP).
• Familiarity with ETL tools and frameworks (e.g., IICS, Apache Airflow, Talend) for automating data workflows.
• Collaborating with data analysts and data scientists to understand data requirements and provide necessary infrastructure support.
• Monitoring and optimizing data pipelines and systems to ensure efficient performance and minimal downtime.
• Documenting data processes, data flows, and system architecture for knowledge sharing and future reference.
• Staying updated with emerging technologies and trends in data engineering and analytics.
• Identifying opportunities for automation, efficiency improvements, and scalability enhancements within data engineering processes.

Better is out there. Better medications, better outcomes, better science. But progress doesn’t happen without people – people from different backgrounds, in different locations, doing different roles, all united by one thing: a desire to make miracles happen. So, let’s be those people.