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Metro location, common data stack, and mid-sized brand yield moderate applicant density.
Core data engineering skills transferable, while on-premise/Hadoop experience increases role-specific specialization.
Explicit 8+ years plus mandatory Python, Spark, Airflow, SQL, and leadership increases filtering rigor.
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Lead and manage a data engineering team responsible for designing and delivering scalable on-premise data pipelines.
Design, develop, and optimize ETL/ELT workflows using Python, Apache Spark, SQL, and Apache Airflow.
Oversee technical design, project planning, code reviews, and ensure data pipeline reliability and performance.
8+ years of experience in Data Engineering.
Strong hands-on experience with Python, Apache Spark/PySpark, SQL (complex queries, optimization), and Apache Airflow.
Experience working in on-premise data environments with large-scale data processing.
Proven experience managing or leading Data Engineering teams.
Experienced in end-to-end ownership of data pipeline architecture and delivery in on-premise setups.
Skilled at mentoring engineers and driving technical direction and project execution in Agile environments.
Strong at stakeholder management and cross-functional collaboration to translate requirements into technical solutions.