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Metro location, mid-level Data Engineer title, 2+ years requirement, and known pharma brand increase competition.
Core data engineering skills are transferable, but pharmaceutical scientific data domain knowledge raises fit sensitivity to medium.
Explicit 2+ years, specific data platform skills, and regulated pharmaceutical context increase shortlisting rigor.
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Build and maintain scalable scientific data pipelines and data products supporting pharmaceutical product development datasets (molecular features, material properties, lab data, manufacturing parameters, stability, performance).
Contribute to data structuring, contextualization, and automation to improve accessibility, reliability, and quality of data for AI, analytics, and scientific modeling.
Collaborate across US, Europe, and India teams on cloud-native architectures, Databricks, Data Fabric, and reusable data assets to support innovation in product development.
Bachelor's or Master’s degree in Computer Science, Chemical Engineering, Information Systems, Bioinformatics, Biotechnology or related field.
2+ years of industry experience in relevant data engineering roles.
Hands-on experience with SQL, Python, ETL/ELT development, Databricks, Lakehouse architectures and modern data platforms.
Work Experience Required: Minimum 2 years in data engineering or related roles, preferably with pharmaceutical or scientific datasets.
Experienced in handling complex scientific/manufacturing/laboratory datasets with understanding of domain-specific data and AI/ML workflows.
Skilled in modern data engineering tools including PySpark, Python, dbt, Databricks, and containerized/cloud infrastructure.
Able to work cross-functionally with scientists, analysts, and engineers to translate messy, ambiguous data problems into scalable AI-ready data solutions.