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Metro location plus broad multi-cloud and leadership-focused data skillset create moderate competition.
Requires deep data engineering and cloud expertise, so skills are less transferable across unrelated domains.
Explicit 8–12 years plus mandatory cloud, ETL, leadership, and governance skills make selection highly selective.
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Manage and mentor a team of Data Engineers, overseeing performance and professional growth.
Own and optimize the enterprise Data Lake architecture, including design and execution of ETL/ELT pipelines from enterprise systems like SAP and Salesforce.
Lead compliance with data governance, data quality frameworks, and cost optimization of cloud data platforms, collaborating with cross-functional teams to deliver scalable data solutions.
8-12 years of Data Engineering experience with 3-5 years in a managerial or leadership role.
Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or related field.
Expertise in at least one major cloud platform (Azure, AWS, or GCP) with strong SQL, Python, and PySpark skills for ETL and automation.
Experience with data lakehouse architectures, big data technologies (Hadoop, Spark), data modeling, and integration with enterprise systems like SAP and Salesforce.
Proven ability to lead distributed Data Engineering teams and manage large-scale, multi-system enterprise data environments.
Experience driving data governance, security compliance, and cost optimization on cloud infrastructure.
Strategic operator comfortable bridging technical execution with senior leadership collaboration to support AI/ML initiatives and enterprise data strategies.