Data Engineering Manager, Commercial

Job Title: Data Engineering Manager, Commercial
Location: Morristown, NJ

• 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

• 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…