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Strong employer brand, popular mid-level Data Engineer title, metro location, and broad skillset increases candidate competition.
Core data engineering skills transfer across industries, but pharmaceutical scientific dataset experience increases domain sensitivity.
Explicit 2+ years requirement and specific Databricks/dbt/AWS data-engineering skills increase screening rigor.
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Build and maintain scalable data pipelines and scientific data products supporting pharmaceutical product development datasets (molecular, material, lab, process parameters, stability, performance).
Contribute to data structuring, contextualization, and support enterprise initiatives involving Databricks, Data Fabric, cloud-native architectures across US, Europe, and India.
Automate data workflows to improve data accessibility, reliability, and quality for analytics, AI, and scientific modeling.
Bachelor's or Master's degree in Computer Science, Chemical Engineering, Information Systems, Bioinformatics, Biotechnology or related field.
2+ years of industry experience in data engineering or related role.
Technical skills required: SQL, Python, ETL/ELT, Delta Lake, Lakehouse Architecture, Databricks, AWS, Data Modeling, Data Warehousing, Data Governance concepts.
Work Experience Required: Minimum 2 years industry experience as per qualifications.
Experience or strong familiarity with pharmaceutical product development datasets including scientific, manufacturing, or laboratory data preferred.
Comfortable working with modern data engineering technologies and cloud platforms focused on AI/ML data foundations (Databricks, Delta Lake, Lakehouse).
Strong analytical skills to solve ambiguous scientific data problems and collaborate with cross-functional scientific and technical teams.