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Popular mid-senior data role in a metro with broad Databricks/Spark requirements increases candidate competition.
Data engineering skills transferable across industries, but Databricks/Spark specialization raises moderate sensitivity.
Explicit 6–10 years requirement and mandatory Databricks, Spark, Python, Java, SQL make filters strict.
Design, develop, and optimize scalable ETL/ELT data pipelines and data platforms primarily using Databricks, Apache Spark, Python, Java, and SQL.
Implement robust data ingestion, transformation, processing, validation frameworks, and perform performance tuning on Spark jobs, SQL queries, and pipelines handling large-scale datasets.
Collaborate with data, cloud, analytics, and engineering teams to deliver scalable, production-grade data engineering solutions, troubleshoot production issues, and mentor junior engineers.
6+ years of professional experience in data engineering/software development with delivery of production-grade data solutions.
Strong hands-on experience with Databricks and Apache Spark for building scalable ETL/ELT pipelines.
Proficiency in Python, Java, and advanced SQL including query optimization and working with large datasets.
Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or relevant technical field.
Experienced in designing and maintaining scalable, distributed data processing solutions in enterprise environments using modern data engineering tools and frameworks.
Comfortable working in Agile teams collaborating cross-functionally including cloud and analytics groups.
Familiarity with cloud-based data platforms (preferably AWS), Delta Lake, and CI/CD/DevOps practices applied to data engineering is advantageous.