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Tier-1 brand, mid-level popular data-engineer title, metro location, and broad skill requirements drive high competition.
Data engineering skills are transferable across industries but require Spark/Databricks expertise, yielding medium sensitivity.
Explicit 5–8 years, mandatory Spark/PySpark/Python and leadership requirements 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 to deliver high-quality data engineering projects.
Collaborate with stakeholders to translate business needs into technical requirements and ensure project delivery within timelines and budgets.
5-8 years of experience in data engineering or related roles.
Strong hands-on experience with Spark, PySpark, and Python in building scalable data pipelines.
Bachelor's degree in Engineering (B.Tech) or equivalent (M.Tech/MCA/MBA also acceptable).
Experience with cloud-native data engineering platforms like Databricks, Azure Data Engineering, or AWS is highly desirable.
Experienced in data architecture and data platform design with strong operational leadership skills to manage teams.
Proficient in end-to-end data engineering including data quality, security, ETL, and data warehousing concepts.
Familiar with cloud data services, containerization (Docker, Kubernetes), and able to work cross-functionally to align technical implementations with business goals.