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Metro role with common data manager title but niche pharma specialization reduces candidate pool.
Requires pharma commercial data expertise and MDM knowledge, making industry-specific fit high.
Explicit 7–13 years, mandatory pharma domain, and specific cloud/Databricks/PySpark requirements increase filter strictness.
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Lead end-to-end data engineering initiatives for pharma clients including designing scalable cloud-based data architecture and data pipelines.
Manage and optimize data lakes, warehouses, and implement data quality frameworks.
Lead and manage a cross-functional team of data engineers and analysts while collaborating with stakeholders and clients.
7-13 years of total experience in data engineering with proven enterprise-scale implementation experience.
Strong expertise in Azure or AWS, Databricks, Pyspark, Python, SQL, and Redshift relevant to pharma data platforms.
Mandatory pharma or life sciences domain expertise, specifically in commercial functional areas and commercial pharma data sources.
Experience in client-facing roles and project delivery; Excellent communication skills.
Experienced leader with ability to manage teams and projects in a pharma data engineering context.
Deep understanding of scalable data architectures and cloud-native platforms tailored for pharma analytics.
Skilled at stakeholder management and delivering client-centric solutions in commercial pharma data environments.