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Metro Bangalore, mid-level generalist role with common Python/Spark skills increases candidate competition.
Core data engineering skills are broadly transferable across industries despite some GenAI-specific preferences.
Explicit 5–6+ years, mandatory Python/Spark/Airflow/cloud skills make filters strict.
Design and build scalable, reliable data pipelines using PySpark and big data technologies.
Collaborate with data science to develop features improving model accuracy and performance.
Troubleshoot, optimize ETL workflows, conduct POCs for new technologies, and maintain technical documentation.
5–6+ years of hands-on experience in Data Engineering.
Strong proficiency in Python, advanced SQL, and Apache Spark.
Experience with Apache Airflow, FastAPI, and at least one major cloud platform (AWS, Azure, or GCP).
Foundational understanding of LLMs, AI/GenAI concepts, and familiarity with vector databases, semantic search, knowledge graphs, and preferably RAG architectures.
Experienced in building production-scale data pipelines and integrating AI/ML features collaboratively.
Practically skilled in workflow orchestration, backend development, and cloud infrastructure.
Comfortable evaluating and integrating cutting-edge data and AI technologies into existing platforms.