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Popular Data Engineer title but senior level and specialized Spark/Scala/GenAI skills create moderate competition.
Core data engineering skills are broadly transferable across industries despite GenAI additions.
Requires 9+ years and specific Spark/Scala/AWS/GenAI skills, so strict technical shortlisting.
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Design and deliver scalable end-to-end data engineering solutions aligned with business goals using Spark, Scala, and AWS cloud.
Build and maintain large-scale ETL pipelines and data architectures incorporating Big Data platforms including Hadoop, Hive, and Teradata.
Lead or contribute to technical design and implementation, collaborating across business and engineering teams, ensuring security and compliance with governance standards.
9+ years of experience in Data Engineering, Analytics, or Data Warehousing.
Strong proficiency in Spark, Scala, AWS cloud data engineering, Oracle SQL/PL-SQL, Teradata, Hadoop, and Hive.
Experience with ETL pipeline development and knowledge of GenAI frameworks such as Lang Chain, Llama, or Hugging Face.
Python skills preferred but not explicitly mandatory.
Experienced senior data engineer skilled in designing scalable, secure, and enterprise-compliant data solutions in Agile environments.
Candidate with strong technical leadership and cross-functional collaboration experience across business and engineering teams.
Familiarity with deploying or integrating GenAI frameworks within data engineering solutions.