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Tier-1 brand, metro location, mid-level generalist data engineering role attracts strong applicant competition.
Core data engineering skills transferable, but consulting and stakeholder expectations increase domain specificity.
Explicit 5-8 years and mandatory Spark/PySpark data engineering skills make filters stringent.
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Design, develop, and maintain scalable data pipelines and architectures using Spark, PySpark, and Python.
Lead and manage a team of data engineers including technical guidance, mentorship, and performance management.
Manage project timelines, resources, and budgets to ensure successful delivery of data engineering projects while ensuring data quality, integrity, and security.
5-8 years of experience in data services, data architecture, and data platforms with hands-on background in data engineering.
Mandatory strong experience with Spark, PySpark, and Python for data engineering technologies.
Bachelor's degree in Engineering (B.Tech) or relevant fields such as M.Tech, MCA, or MBA.
Experience with cloud-native data engineering platforms like Databricks, Azure Data Engineering, or AWS is highly desirable.
Experienced in leading data engineering teams and projects with a focus on delivering scalable data pipelines and solutions.
Skilled in cloud platforms for data storage, processing, and analytics (e.g., Databricks, Azure, AWS) with familiarity in containerization (Docker, Kubernetes).
Strong understanding of data warehousing, ETL processes, data quality, and best practices in code review and documentation.