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Mid-level, metro-based Databricks data engineer role with common title increases applicant competition.
Core data engineering skills are widely transferable, though Databricks specialization moderately narrows industry fit.
Explicit 5–10 year requirement plus mandatory Databricks/PySpark and Delta Lake skills make shortlisting strict.
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Design, develop, and maintain scalable ETL/ELT data pipelines using Databricks and PySpark.
Build and optimize data lakehouse architectures including Bronze–Silver–Gold layers and implement Delta Lake features such as schema evolution and ACID transactions.
Support production deployments, monitor and troubleshoot data pipelines; collaborate with cross-functional teams to deliver high-performance data platforms.
5–10 years of experience in Data Engineering with minimum 2–4 years of hands-on Databricks experience.
Strong expertise in Databricks (Workflows, Notebooks, Jobs), PySpark/Spark SQL, SQL and Data Warehousing concepts.
Experience with cloud platforms like AWS (S3, Glue, EMR) or Azure (ADF, ADLS, Synapse).
Location: India with hybrid work model requiring 3 days onsite.
Proven experience building enterprise-scale data platforms and pipelines with a focus on Databricks and Delta Lake technologies.
Comfortable working in a fast-paced, agile environment with strong problem-solving and analytical skills.
Experience collaborating with analytics, product, and business teams to translate requirements into scalable data solutions.