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Tier-1 brand, popular Data Engineer title, metro locations, and mid-level experience increase competition.
Core data engineering skills transfer broadly, but Databricks-on-Azure specialization raises moderate domain specificity.
Explicit 6–10 years plus mandatory Databricks/Azure and PySpark requirements make shortlisting stringent.
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Design, develop, and deploy enterprise-scale data engineering solutions primarily using Databricks on Azure.
Lead technical discussions, prepare architecture/designs, manage project deliverables, and mentor junior team members throughout project lifecycle.
Develop and enforce technical standards and automate development/operational tasks for data pipelines and platform management.
6–10 years of data engineering experience with strong expertise in Databricks on Azure.
Proficient in Python, SQL, PySpark; knowledge of Lakehouse architecture, Apache Spark, Delta Lake mandatory.
Bachelor’s degree in BE/B.Tech/MCA/M.Sc (CS) or equivalent from an accredited university.
Work Experience Required: 6–10 years in relevant data engineering roles.
Experienced in building metadata-driven ingestion and data quality frameworks using PySpark.
Skilled in large-scale data platform modernization including governance and integration of structured and unstructured data using cloud technologies.
Comfortable leading cross-functional teams, managing project executions, and working in hybrid cloud and AI/ML environments.