Data Engineering Manager, Commercial US
Sanofi
June 25, 2026
Remote friendly (Morristown, NJ)
United States
$148,500 - $214,500 USD yearly
IT
Main Responsibilities:
- Lead, mentor, and develop a team of data engineers; foster strong engineering practices, accountability, collaboration, and continuous improvement.
- Design, build, deploy, and support scalable data pipelines and data products for analytics, AI/ML, and commercial use cases.
- Lead discovery, solution design, and technical planning discussions with business, analytics, AI, and technology teams.
- Manage delivery, priorities, capacity planning, and execution across multiple concurrent data engineering initiatives.
- Design, develop, test, and optimize reusable data engineering solutions and assets for analytics, AI/ML, and business-critical workflows.
- Partner with technical and non-technical stakeholders to clarify needs, shape approaches, and translate requirements into scalable solutions.
- Provide architectural/technical leadership across orchestration, distributed processing, cloud-native platforms, and integration patterns.
- Drive operational excellence for production data assets (monitoring, troubleshooting, incident response, release management).
- Automate, simplify, standardize, and optimize data engineering processes, assets, and platform capabilities.
- Collaborate in cross-functional agile teams; evolve standards, best practices, and knowledge sharing.
- Stay current with emerging technologies and modern data engineering practices.
Qualifications:
- 6+ years in data engineering/analytics engineering/data platform development; 2+ years leading/managing engineering teams.
- Experience building/operating scalable pipelines/platforms using Spark, Kafka, Snowflake, Hadoop (or similar).
- Cloud-native data engineering and modern ETL/ELT (preferably Snowflake/AWS); Informatica/IICS preferred.
- Advanced SQL and data modeling; Python/scripting; Scala/Java a plus.
- Batch, near real-time, and streaming architectures; warehouse/lake/lakehouse concepts (data mesh).
- Strong data architecture, scalability, reliability, performance optimization, and operational support.
- Ability to work with technical and non-technical stakeholders; navigate ambiguity and translate needs into execution plans.
- Strong communication/facilitation; can influence decisions and explain complex concepts.
- Cross-functional partnership (analytics, AI/ML, product, infrastructure, security, governance, business).
- Agile delivery experience; CI/CD, release management, testing, operational support.
- Experience leading teams through delivery execution, prioritization, mentoring, performance management, continuous improvement.
- Bachelor’s/Master’s in CS/Engineering/STEM/Business or equivalent experience.
Nice to Haves:
- Life sciences/healthcare/pharmaceutical experience.
- Airflow, dbt (or similar) orchestration/transformation tooling.
- Data governance/data quality; commercial domains (omni-channel, pricing, customer engagement, sales analytics).
- Experience with external vendors and offshore/onshore delivery models.
- Lead, mentor, and develop a team of data engineers; foster strong engineering practices, accountability, collaboration, and continuous improvement.
- Design, build, deploy, and support scalable data pipelines and data products for analytics, AI/ML, and commercial use cases.
- Lead discovery, solution design, and technical planning discussions with business, analytics, AI, and technology teams.
- Manage delivery, priorities, capacity planning, and execution across multiple concurrent data engineering initiatives.
- Design, develop, test, and optimize reusable data engineering solutions and assets for analytics, AI/ML, and business-critical workflows.
- Partner with technical and non-technical stakeholders to clarify needs, shape approaches, and translate requirements into scalable solutions.
- Provide architectural/technical leadership across orchestration, distributed processing, cloud-native platforms, and integration patterns.
- Drive operational excellence for production data assets (monitoring, troubleshooting, incident response, release management).
- Automate, simplify, standardize, and optimize data engineering processes, assets, and platform capabilities.
- Collaborate in cross-functional agile teams; evolve standards, best practices, and knowledge sharing.
- Stay current with emerging technologies and modern data engineering practices.
Qualifications:
- 6+ years in data engineering/analytics engineering/data platform development; 2+ years leading/managing engineering teams.
- Experience building/operating scalable pipelines/platforms using Spark, Kafka, Snowflake, Hadoop (or similar).
- Cloud-native data engineering and modern ETL/ELT (preferably Snowflake/AWS); Informatica/IICS preferred.
- Advanced SQL and data modeling; Python/scripting; Scala/Java a plus.
- Batch, near real-time, and streaming architectures; warehouse/lake/lakehouse concepts (data mesh).
- Strong data architecture, scalability, reliability, performance optimization, and operational support.
- Ability to work with technical and non-technical stakeholders; navigate ambiguity and translate needs into execution plans.
- Strong communication/facilitation; can influence decisions and explain complex concepts.
- Cross-functional partnership (analytics, AI/ML, product, infrastructure, security, governance, business).
- Agile delivery experience; CI/CD, release management, testing, operational support.
- Experience leading teams through delivery execution, prioritization, mentoring, performance management, continuous improvement.
- Bachelor’s/Master’s in CS/Engineering/STEM/Business or equivalent experience.
Nice to Haves:
- Life sciences/healthcare/pharmaceutical experience.
- Airflow, dbt (or similar) orchestration/transformation tooling.
- Data governance/data quality; commercial domains (omni-channel, pricing, customer engagement, sales analytics).
- Experience with external vendors and offshore/onshore delivery models.