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Tier-1 employer, metro location, popular data engineering role with common tech stack increases applicant competition.
Enterprise-scale data platform skills transfer broadly, though pharma domain knowledge adds moderate bias.
Explicit 12+ years, 5+ leadership, and mandatory Databricks/Spark/AWS requirements make shortlisting stringent.
Lead and develop data engineering teams to deliver enterprise-scale data platforms and products aligned with strategic business priorities.
Drive modernization of Enterprise Data Fabric and implement scalable, secure data solutions supporting analytics, AI, and digital transformation.
Oversee delivery lifecycle including design, development, deployment, and support of data products, managing risks, vendor execution, and engineering standards.
12+ years in data engineering, data platforms, analytics engineering, or related fields.
5+ years leadership experience managing engineering teams and large-scale delivery programs.
Strong technical expertise with Databricks, Spark, PySpark, SQL, Python, AWS, and cloud-native data architectures.
Experience with Data Fabric concepts, metadata management, governance, and Agile or SAFe delivery environments.
Experienced in strategic leadership of enterprise data engineering initiatives involving multiple business domains and global teams.
Demonstrated success in modern data platform architectures including Data Fabric, Data Mesh, and Lakehouse concepts.
Comfortable managing multi-vendor teams and influencing cross-functional stakeholders to achieve measurable business outcomes.