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Mid-level, generalist data engineer in Bangalore with broad Kafka/Spark/Snowflake requirements increases competition.
Data engineering skills like Kafka, Spark, and Snowflake are highly transferable across industries.
Explicit 5+ years plus mandatory Kafka, Spark, Snowflake skills create strict technical filtering.
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Design, develop, test, and maintain scalable batch and streaming data pipelines across enterprise sources.
Build and support data ingestion, transformation, and integration using Kafka/Confluent, Snowflake, Spark, Python, and modern lakehouse technologies.
Contribute to foundational enterprise data platform capabilities supporting AI initiatives like Prism with governance, lineage, and data quality.
5+ years of software engineering or data engineering experience; 2+ years specializing in data platforms, streaming systems, ETL, or data integration.
Bachelor’s degree or equivalent experience.
Strong skills in SQL (e.g., SQL Server, Snowflake, PostgreSQL) and hands-on experience with Kafka/Confluent, Spark, Python, Snowflake; familiarity with modern lakehouse technologies (Iceberg, Delta Lake).
Experience with data governance, cataloging, lineage, and data quality is preferred but not strictly mandatory.
Experienced with designing scalable, secure, and maintainable data platform components for enterprise use cases, including streaming systems and batch pipelines.
Able to collaborate effectively with architects, product managers, and engineers to deliver reliable data platform services supporting AI and analytics.
Demonstrates capability to review technical designs, enforce coding standards, and mentor junior developers with a focus on data engineering best practices.