Data Engineering Manager, Commercial US

About the Job

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, to accelerate R&D, manufacturing and commercial performance and bring better drugs and vaccines to patients faster, to improve health and save lives.

You are a hands-on data engineering leader with a strong background in building and operating data pipelines and platforms in complex enterprise environments. You bring a blend of technical depth, architectural thinking, and people leadership. You are comfortable guiding a team, partnering with stakeholders, and translating business needs into scalable and reliable data solutions. You care about engineering quality, operational excellence, and continuous improvement. You have experience working across cross-functional teams and know how to balance delivery, governance, and long-term platform sustainability.

In this role, you will report directly to the Head of Data Engineering. You will provide architectural leadership and technical know-how across data pipeline construction, execution, and operations. You will work closely with teams across Data, Digital, Infrastructure, Cloud, Security, Agile, and key business stakeholders. You will also have direct people management responsibilities and the opportunity to shape and grow your team.

Main Responsibilities

• Lead, mentor, and develop a team of data engineers while fostering strong engineering practices, accountability, collaboration, and continuous improvement
• Design, build, deploy, and support scalable data pipelines and data products that enable analytics, AI/ML, and commercial business use cases
• Lead discovery, solution design, and technical planning discussions with business, analytics, AI, and technology teams
• Manage team delivery, priorities, capacity planning, and execution across multiple concurrent data engineering initiatives
• Design, develop, test, and optimize scalable data engineering solutions and reusable data assets that support analytics, AI/ML, and business-critical workflows across global platforms
• Partner with technical and non-technical stakeholders to clarify ambiguous business needs, shape solution approaches, and translate requirements into scalable data engineering solutions
• Provide architectural and technical leadership across data pipeline orchestration, distributed processing, cloud-native platforms, and data integration patterns
• Drive operational excellence across production data assets, including monitoring, troubleshooting, incident response, release management, and continuous improvement
• Identify opportunities to automate, simplify, standardize, and optimize data engineering processes, reusable assets, and platform capabilities
• Collaborate within cross-functional agile teams and partner with internal and external stakeholders to deliver high-quality data engineering solutions
• Contribute to and evolve data engineering standards, best practices, and community knowledge sharing across the organization
• Stay current with emerging technologies, industry trends, and modern data engineering practices to continuously improve platform capabilities and engineering effectiveness

Qualifications

• 6+ years of experience in data engineering, analytics engineering, or data platform development, including 2+ years leading or managing engineering teams
• Demonstrated experience designing, building, and operating scalable data pipelines, data platforms, and distributed processing solutions using technologies such as Spark, Kafka, Snowflake, Hadoop, or similar
• Strong experience with cloud-native data engineering and modern ETL/ELT solutions, preferably within Snowflake / AWS-based environments; Informatica/IICS experience preferred
• Advanced SQL and data modeling skills, with working knowledge of Python and scripting languages; Scala or Java is a plus
• Experience with batch, near real-time, and streaming data architectures, as well as modern data warehouse, lake, and lakehouse concepts including data mesh principles
• Strong understanding of data architecture, scalability, reliability, performance optimization, and operational support for enterprise-grade data platforms
• Demonstrated ability to work with technical and non-technical stakeholders to navigate ambiguity, identify underlying business needs, and translate them into scalable technical solutions and execution plans
• Strong communication, facilitation, and stakeholder management skills, with the ability to influence decisions and communicate complex technical concepts to diverse audiences
• Experience partnering with cross-functional teams including analytics, AI/ML, product, infrastructure, security, governance, and business stakeholders
• Experience operating in agile delivery environments with strong understanding of software engineering practices, CI/CD, release management, testing, and operational support
• Experience leading engineering teams through delivery execution, prioritization, mentoring, performance management, and continuous improvement initiatives
• Bachelor’s or Master’s degree in Computer Science, Engineering, STEM, Business, or a related field, or equivalent practical experience

Nice to Haves

• Experience in life sciences, healthcare, or pharmaceutical industries
• Experience with Airflow, dbt or similar orchestration and transformation tooling
• Familiarity with data governance, data quality, and commercial data domains such as omni-channel, pricing, customer engagement, or sales analytics
• Experience working with external vendors and offshore/onshore delivery models