Director of Data Engineering & Analytics (Hybrid)

Merck & Co.; Rahway (NJ); Hybrid; USD 191,000 - 300,000; Full time

Job summary

Merck & Co. in Rahway, NJ seeks a Director of Data Engineering and Analytics to lead a cross-functional team across SM S&T, IT, and manufacturing operations. This role translates data priorities into executable programs, delivering measurable business value and scaled analytics capabilities.

The ideal candidate will drive data pipeline development, ensure data quality, and enable scientists to leverage analytics for process monitoring and lifecycle management in a highly matrixed environment.

Qualifications

• BS in engineering or related technical discipline with ten years of required experience (MS with 8).
• Demonstrated leadership in pharmaceutical manufacturing, technical operations, data engineering, or data science.

Responsibilities

• Drive execution of data and analytics priorities across SM S&T with measurable business impact.
• Translate data and analytics strategy into practical plans, milestones, and value realization metrics.
• Partner with SM S&T leadership, Digital Manufacturing, Digital CMC, MPI, IT, and site teams to align priorities.
• Ensure analytics initiatives deliver clear business value and are anchored in real problems.
• Lead data engineering initiatives to build robust data pipelines and ensure data quality.
• Enable scientists and engineers to access data for process monitoring and lifecycle management.

Skills

Data engineering

Data analytics

SQL databases

Seeq

MPI

Process monitoring

Analytics strategy

Executive leadership

Education

BS in Engineering, Pharmaceutical Sciences, Data Science, IT, or CS

MS degree preferred

Tools

DataLynx

SEEQ

MPI

MES

Historians

Job description

Merck & Co. in Rahway, NJ seeks a Director of Data Engineering and Analytics to lead a cross-functional team across SM S&T, IT, and manufacturing operations. This role translates data priorities into executable programs, delivering measurable business value and scaled analytics capabilities.

The ideal candidate will drive data pipeline development, ensure data quality, and enable scientists to leverage analytics for process monitoring and lifecycle management in a highly matrixed environment.