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Tier-1 brand, metro location, mid-level generalist data role with broad cloud skills.
Technical data engineering skills are transferable, though advisory/consulting experience is preferred.
Explicit 5-8 years plus mandatory Spark/PySpark/Python and cloud requirements raise filter strictness.
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Lead design, development, and maintenance of scalable data pipelines and architectures using Spark, PySpark, and Python.
Manage and mentor a team of data engineers, overseeing technical guidance and performance management.
Collaborate with cross-functional teams and stakeholders to deliver high-quality, secure data solutions aligned with business requirements.
5-8 years of experience in data engineering or related roles.
Strong hands-on experience with Spark, PySpark, and Python in data pipeline development.
Bachelor's degree in Engineering (B.Tech) or related fields; M.Tech/MCA/MBA are acceptable.
Experience with cloud-native data engineering platforms like Databricks, Azure Data Engineering, or AWS is highly desirable.
Demonstrates strong leadership and team management capabilities in a senior data engineering role.
Hands-on expertise in scalable data pipeline architectures and data processing using Spark and PySpark.
Experience working with cloud data engineering platforms and familiarity with cloud data storage, processing, and analytics services.