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Tier-1 employer and metro location increase applicant density, but senior specialization moderates competition.
Core data engineering skills are broadly transferable across industries despite pharma domain preference.
Explicit 9+ years requirement plus mandatory modern data stack and leadership skills makes screening strict.
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Lead development of core data components and pipelines for advanced analytics and AI applications in the commercial pharma domain.
Collaborate cross-functionally to execute enterprise data strategy through technical design and solution delivery with modern data stack technologies.
Deliver high-quality, performant data solutions enabling statistical analysis, machine learning, retrieval-augmented generation, and contribute to impactful commercial and brand strategic decisions.
9+ years of experience in data or analytics engineering.
Bachelor’s, Master’s, or PhD in Computer Science, Statistics, Data Science, Engineering, or related quantitative field.
Strong proficiency in Python including libraries such as Polars, Pandas, and Numpy.
Hands-on experience with modern data stack technologies including dbt, Airflow, Spark, Snowflake, SQL/NoSQL databases, data quality frameworks, and software development workflows like Git, CI/CD, and Docker.
Experienced leader capable of mentoring engineers and managing technical projects in enterprise data environments.
Deep expertise in building and maintaining large-scale, performant distributed data systems tailored for cutting-edge AI/ML use cases in pharma.
Proven ability to translate complex data strategies into operational data pipelines and products that drive measurable business impact in commercial analytics.