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Strong Tier-1 brand, metro location, and in-demand Databricks skills create medium competition.
Databricks/PySpark specialization moderately reduces cross-industry portability though core data-engineering skills transfer.
Explicit 7–10 years requirement plus mandatory Databricks and PySpark make filtering strict.
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Lead development of data and analytics products using Databricks technologies, managing the process from requirement gathering to driving user adoption.
Design, develop, and optimize scalable ETL/ELT pipelines with PySpark and Databricks, ensuring data quality, integrity, and governance.
Collaborate with data scientists, analysts, and stakeholders to deliver high-quality, cost-efficient data processing solutions using cloud services and Databricks platform.
7-10 years of professional experience, including 5+ years in data and analytics and 5-8 years working with Databricks tech stacks.
Strong proficiency in PySpark, Databricks, Apache Spark, SQL, and BI/Data-warehousing concepts.
Education: Bachelor of Technology or Master of Engineering degree (BTech/MEng/MBA mentioned).
Experience in developing and optimizing ETL processes and data pipelines using cloud storage (Azure Data Lake, AWS S3) and ensuring data governance.
Experienced in leading full lifecycle development of data analytics products in a global organization environment.
Skilled in performance tuning and cost-optimization of Spark jobs on Databricks platform with strong understanding of data transformation tools like Unity Catalog, Delta Tables, DLT.
Capable of collaborating across teams to translate business needs into scalable technical solutions with documented and tested pipelines.