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Tier-1 brand, popular data-engineer role, metro location, and broad skillset requirements increase competition.
Data engineering skills are broadly transferable across industries despite domain-specific governance knowledge.
Explicit 8–12 years and mandatory Databricks/PySpark plus shift requirements increase strictness.
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Design, develop, and maintain end-to-end scalable data pipelines and ETL solutions using big data technologies (Databricks, PySpark, SparkSQL).
Own data pipeline projects from inception to deployment including scope, timelines, and risk management.
Collaborate with cross-functional and global teams to meet business data requirements, ensure data quality, governance, and optimize big data processing performance.
8 to 12 years of experience in Computer Science, IT, or related field with hands-on big data expertise.
Proficiency with Databricks, Apache Spark (PySpark, SparkSQL), SQL, and performance tuning of big data workloads.
Bachelor’s or Master’s degree in Computer Science, IT or related field.
Willingness to work evening or night shifts as required due to business needs.
Experienced in building and optimizing large-scale data pipelines in cloud environments, preferably AWS and Databricks.
Demonstrated ability to manage complex data projects end-to-end with accountability for delivery and cross-team collaboration.
Skilled at handling structured and unstructured data integration, data governance, and performance optimization in a fast-paced, distributed global team setting.