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Tier-1 brand, mid-level Databricks data engineer in metro with common Spark skills increases competition.
Databricks and Spark data engineering skills are highly transferable across industries.
Explicit 5-8 years requirement plus mandatory Databricks, Spark, Delta Lake and cloud skills enforce strict shortlisting.
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Design, build, and maintain scalable, high-performance end-to-end data pipelines on the Databricks Lakehouse Platform using Apache Spark, PySpark, Spark SQL, and Delta Lake.
Collaborate closely with analytics, AI/ML, and business teams to support data ingestion, transformation, quality, governance, and downstream consumption use cases.
Optimize Spark jobs for performance and cost efficiency, provide production support, troubleshoot data pipeline issues, and mentor junior engineers.
5 to 8 years of experience as a Data Engineer with strong expertise in Databricks and Apache Spark ecosystem (PySpark, Spark SQL).
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field with at least 60% marks.
Experience with Delta Lake, Lakehouse architecture, ETL/ELT patterns, and at least one cloud platform (Azure/AWS/GCP).
Advanced SQL skills and understanding of distributed computing concepts.
Experienced in implementing batch and incremental data processing, preferably with knowledge of Delta Lake multi-hop architecture and data warehousing concepts.
Familiar with Agile team environments and capable of collaborating cross-functionally with data scientists, analysts, and architects on AI/ML workloads.
Practiced in performance optimization of Spark jobs, production support, documentation of technical designs, and mentoring junior engineers.