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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 employer, mid-level Data Engineer title, metro location, and broad skillset increase applicant competition.
Technical data engineering skills are transferable, though pharmaceutical supply-chain experience is preferred, creating moderate domain bias.
Explicit 5–8 years plus mandatory Databricks, PySpark, SQL, AWS, and BI skills impose high filtering.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and maintain scalable data pipelines and solutions using big data technologies and cloud platforms (AWS preferred) to support global supply chain analytics.
Create and manage ETL processes ensuring data quality, and develop data visualizations and dashboards (Tableau, Power BI) to provide actionable business insights.
Support data governance by maintaining data models, documentation, and compliance-related reporting (annual product reviews, regulatory inspections).
Minimum Requirements
Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent.
5–8 years of relevant work experience in IT or Information Systems; pharmaceutical industry experience preferred.
Hands-on experience with big data technologies (Databricks, PySpark, SparkSQL), ETL processes, SQL, and data visualization tools (Tableau, Power BI).
Proficiency in Python/R for exploratory data analysis, feature engineering, and machine learning model training.
Ideal Candidate Profile
Experienced in designing and optimizing end-to-end data pipelines for analytics in a global supply chain or pharmaceutical business context.
Strong technical expertise in big data ecosystems, cloud platforms (preferably AWS), and creating self-service analytics platforms for business users.
Capable of maintaining data accuracy, governance, and documentation while supporting complex data workflows and regulatory documentation requirements.