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Tier-1 brand, Bangalore location, mid-level data engineer title and broad cloud/Spark requirements make competition high.
Core data engineering skills (Spark, Databricks, Python) are highly transferable across industries.
Explicit 5–8 years, mandatory Spark/Databricks certification and specific tech skills 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 while overseeing project timelines, resources, and budgets for successful delivery.
Collaborate with cross-functional teams and stakeholders to translate business needs into technical data engineering solutions ensuring data quality, integrity, and security.
5-8 years of experience in data engineering or related roles.
Mandatory certifications: Spark 3.0 and/or Databricks Advanced/Professional Architect.
Strong hands-on experience with Spark, PySpark, Python, and cloud-native data engineering platforms like Databricks, Azure Data Engineering, or AWS.
Required education: Bachelor or Master of Engineering, or MCA/MBA.
Experienced in leading data engineering projects with strong team management and technical leadership skills.
Skilled in designing scalable data pipelines and architectures with deep knowledge of ETL, data warehousing, and cloud services.
Comfortable working in advisory and cross-functional client-facing environments to deliver high-quality, production-grade data solutions.