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Tier-1 brand, metro location, and a mid-level generalist data engineering title increase candidate density.
Core data engineering skills are transferable, but consulting and client-facing experience increases specificity.
Explicit 5–8 years and mandatory Spark/PySpark/Python plus cloud skills make filters stringent.
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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, ensuring project delivery within timelines, resources, and budget.
Collaborate with stakeholders to translate business needs into high-quality technical data solutions while ensuring data quality, integrity, and security.
5-8 years of experience in data engineering or related roles.
Strong hands-on expertise in Spark, PySpark, and Python for data engineering.
Bachelor's degree in Engineering (B.Tech) or equivalent; M.Tech/MCA/MBA also acceptable.
Experience with cloud-native data engineering platforms such as Databricks, Azure Data Engineering, or AWS is highly desirable.
Experienced in leading data engineering projects with a strong technical and leadership background.
Proficient in building scalable data architectures and managing cross-functional collaboration for business impact.
Familiar with cloud computing platforms and modern data engineering best practices including code review, testing, and documentation.