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Tier-1 brand, metro location, mid-level generalist data engineer role create high candidate competition.
Core data engineering skills are transferable across industries, though consulting experience is mildly preferred.
Explicit 5-8 years plus mandatory Spark/PySpark/Python and leadership make shortlisting highly strict.
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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 delivery and project timelines.
Collaborate with cross-functional teams and stakeholders to translate business needs into high-quality data solutions ensuring data quality and security.
5-8 years of experience specializing in data services, data architecture, and data platforms.
Strong hands-on expertise in Spark, PySpark, and Python for data engineering.
Bachelor's degree in Engineering (B.Tech) or equivalent qualifications (M.Tech/MCA/MBA).
Experience with cloud-native data engineering platforms such as Databricks, Azure Data Engineering, or AWS is highly desirable.
Demonstrated leadership in managing data engineering teams and projects with strong technical oversight.
Proven capability to deliver scalable data pipelines integrating data warehousing and ETL best practices.
Experience working with cloud platforms and familiarity with associated services for data storage and processing (e.g., BigQuery, Redshift, S3).