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Metro location and common Data Engineer title increase applicant density, niche on-premise requirements limit applicants.
On-premise Hadoop and enterprise data platform needs increase domain specificity; core data skills partly transferable.
Mandatory 8+ years, leadership, and specific tech stack (Spark, Airflow, on-prem) create strict filtering.
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Lead and manage a Data Engineering team to design, develop, and optimize scalable data pipelines and ETL/ELT workflows in an on-premise environment.
Build and maintain robust Apache Airflow DAGs and Spark-based data processing solutions handling large datasets.
Oversee end-to-end project delivery, including technical design, estimation, mentoring, and ensuring adherence to best practices.
8+ years of experience in Data Engineering with hands-on expertise in Python, Apache Spark/PySpark, SQL, and Apache Airflow.
Experience working specifically in on-premise data environments.
Proven experience in managing or leading Data Engineering teams.
Advanced SQL skills including complex joins, CTEs, window functions, subqueries, and query optimization.
Experienced leader capable of providing technical direction and managing project delivery within Agile environments.
Strong background in designing and optimizing data pipelines and handling large-scale enterprise data platforms, preferably on-premise Hadoop/data ecosystems.
Proficient in stakeholder and client management with ability to mentor and develop data engineering teams effectively.