Associate Director, Data Engineering

Role Overview

• Understand the manufacturing process, process data generation, process data consumption, and analytics usage, and then lead the design, build, and operation of end-to-end data pipelines (ingest → transform → publish) using SQL, Python, and dbt, ensuring reliability, freshness, observability, and performance.
• Build a data strategy to ingest data from both structured and standardized data source systems (e.g., SAP, MES, LIMS) as well as from spreadsheets, paper documents, or other file-based systems.
• Build and own data integrity specifications, and incorporate controls into IT solutions to enforce data quality and integrity.
• Advice on data transformation, context of how data shall be stored, retrieved, and used.
• Collaborate with internal site process SMEs across R&D, CMC, Internal and External manufacturing process SMEs, Digital Services data engineering and data lake teams, manufacturing application software development teams, Suppliers (e.g. Seeq).
• Establish and enforce data engineering standards, reusable pipeline patterns, and data modeling conventions (star/snowflake) aligned with Manufacturing Process Intelligence solution governance.
• Ensure compliance with SDLC, documentation, testing, deployment, and run book expectations, including GMP/GxP data usage considerations.
• Define and monitor SLAs, data quality thresholds, and freshness metrics for prioritized data products.
• Partner with Technical Product Managers and stakeholders to refine backlogs, prioritize investments, and balance speed, quality, and scalability.
• Mentor, and develop data engineers, fostering strong engineering fundamentals, domain understanding, and consistent ways of working.
• Champion data literacy through trainings, office hours, templates, and job aides to enable governed self-service analytics.

Requirements

• Bachelor’s degree in engineering, Computer Science, or a related field.
• minimum 8-10 years of relevant experience in this field.
• Strong expertise in SQL and data modeling with working knowledge of Python.
• Experience designing and operating ETL/ELT pipelines, orchestration, and data quality monitoring.
• Understanding of data lakes, data marts, data ingestion patterns.
• Hands on experience with relational schema design, data warehousing concepts, and performance optimization.
• Knowledge of biopharmaceutical manufacturing processes, generated process data, discrete and time-series data, manufacturing systems (e.g. MES, LIMS, PI, SAP) that generate the data, and scientific TechOps processes that consume the data.

Tech Stack

• ETL
• Python
• SDLC
• SQL

Benefits

• medical, dental, vision healthcare and other insurance benefits (for employee and family)
• retirement benefits, including 401(k)
• paid holidays
• vacation
• compassionate and sick days