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Strong Tier-1 brand, metro location, mid-level generalist data engineering role increases applicant competition.
Data engineering skills are broadly transferable across industries despite consulting context.
Mandatory 5-8 years and required Spark/Databricks certifications make filters highly 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, overseeing technical guidance and performance.
Collaborate with cross-functional teams and stakeholders to deliver high-quality, secure data engineering solutions aligned with business needs.
5-8 years of experience in data engineering or related roles.
Mandatory certification: Spark 3.0 and/or Databricks Advanced/Professional Architect.
Strong hands-on skills in Spark, PySpark, and Python; experience with cloud-native data engineering platforms like Databricks, Azure, or AWS is highly desirable.
Education required: B.Tech / M.Tech / MCA / MBA in relevant fields.
Experienced in leading technical teams and managing data engineering projects end-to-end.
Proficient in designing scalable data architectures and familiar with cloud data services (e.g., Databricks, Azure, AWS).
Able to translate complex business requirements into technical data solutions and enforce best practices in data quality and security.