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Global pharma brand, common mid-level data engineer title, metro location and generalist cloud skills drive high competition.
Cloud ETL skills are transferable across industries, though pharmaceutical data compliance modestly raises domain specificity.
Explicit 5–8 years requirement plus mandatory Python, SQL, and AWS skills increase shortlisting strictness.
Implement and monitor end-to-end data pipelines and ETL jobs to ensure timely, high-quality data delivery at scale across 85+ markets.
Maintain and enhance Python scripts (using Pandas, PySpark) and manage cloud data storage on AWS services like S3, Redshift, and EMR, ensuring data accuracy and compliance.
Lead incident response for pipeline failures, conduct root cause analysis, implement durable fixes, and drive automation to improve reliability and throughput.
Strong proficiency in Python (Pandas, PySpark), SQL, and AWS services including S3, Redshift, and EMR.
Experience with API orchestration tools such as Postman and secure file transfer tools like WinSCP or equivalents.
Work Experience Required: 5–8 years in data engineering, production support, or data operations.
Not explicitly mentioned: mandatory degree requirements, notice period, or specific location constraints.
Experienced in managing large-scale data workflows and cloud-based data pipelines in a production environment.
Background in pharmaceutical or healthcare data ecosystems preferred but not strict requirement.
Demonstrates structured, proactive monitoring, troubleshooting skills, and the ability to coordinate cross-functionally with data providers and DevOps teams.