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Medium — metro location and common Data Engineer title increase applicants, Databricks/PySpark specialization narrows qualified pool.
Low — Databricks, PySpark, and ETL skills are highly transferable across industries.
High — explicit 8–10 years requirement and mandatory Databricks, PySpark, and production support experience.
Design, develop, and maintain scalable data pipelines and enterprise-grade ETL/ELT frameworks using Databricks, PySpark, and SQL on Azure.
Perform production support including incident management, root cause analysis, and troubleshooting of data pipelines and platforms, ensuring data quality, performance, and reliability.
Collaborate with cross-functional teams to implement and optimize cloud-native (Azure/AWS) data solutions, including monitoring, alerting, and operational governance.
8 to 10 years of experience in data engineering, big data, and cloud-based data platforms.
Minimum 5+ years hands-on experience with Databricks and PySpark.
Strong expertise in SQL development including query optimization and performance tuning.
Bachelor's degree in Engineering, Computer Science, Information Technology, or equivalent experience.
Experienced in building and supporting large-scale, distributed data processing solutions in production environments, with emphasis on troubleshooting and operational support.
Proficient in cloud platforms especially Azure (also AWS), with skills in Spark optimization, Delta Lake, and modern Lakehouse architectures.
Capable of owning end-to-end data platform lifecycle including design, development, support, and continuous improvement within Agile teams.