Data Engineering Manager, Commercial US

Sanofi•Morristown, NJ

13d•$148,500 - $214,500•Hybrid

About The Position

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, 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.

Requirements

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

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