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Strong employer brand and common data skills increase competition, but seniority and specialized Databricks/GenAI narrow the pool.
Data engineering and cloud skills are highly transferable across industries, making background fit broadly flexible.
Explicit 11-13 years plus manager experience and specific Databricks, PySpark, and AWS requirements imply strict filters.
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Lead design and implementation of ETL processes for data extraction, transformation, and loading at enterprise scale.
Manage and optimize data engineering team delivery, ensuring production-ready applications and data integrity through rigorous testing.
Collaborate cross-functionally to develop and deliver user-oriented data and analytics solutions using next-generation architecture.
11-13 years of relevant Data Engineering experience with 3-5 years managing data engineering teams.
Experience with GenAI LLM development and testing.
Hands-on expertise in Python, Pyspark, Databricks, Airflow, and AWS services like MWAA, Lambda, SQS, SNS, EC2.
Strong understanding of database concepts including query optimization and performance tuning.
Experienced in managing data engineering teams delivering complex, scalable ETL solutions in agile environments.
Technically proficient with modern cloud (AWS) data tools and scripting languages, emphasizing GenAI and Databricks.
Capable of balancing innovation with simplicity and operational efficiency in data architecture and solution delivery.