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Mid-level generalist data engineer in Bangalore with broad Databricks/Spark/AWS skills increases competition significantly.
Core data engineering skills are broadly transferable, though Databricks/Lakehouse experience is somewhat specialized.
Explicit 2–5 years requirement plus many mandatory Databricks, Spark, AWS, Airflow, and CI/CD skills.
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Build and operate end-to-end data pipelines on Databricks and AWS Lakehouse environment, handling ingestion, transformation, and serving of large datasets.
Design and develop dashboards and reports in Tableau or Superset for business and technical stakeholders to support Enterprise reporting and AI initiatives.
Apply data governance, data quality, and metadata management practices using tools like Unity Catalog and Alation, and support AI data foundations for RAG and agentic use cases.
2–5 years of experience in Data Engineering and Analytics.
Strong proficiency in SQL and Python with hands-on experience on Databricks (including Unity Catalog, Delta Live Tables, Workflows).
Experience with Apache Spark, AWS data stack (S3, Glue, Athena, EMR), Apache Airflow, and managing large datasets using Starburst/Trino/AWS Athena.
Experience with open table formats (Apache Iceberg, Delta Lake), CI/CD via GitHub Actions, and dashboarding tools like Tableau or Superset.
Experience working independently in fast-paced environments, capable of handling multiple priorities with an ownership mindset.
Familiarity with modern Data Lakehouse architectures and keen interest in integrating AI/GenAI technologies into enterprise data solutions.
Strong communication skills with ability to translate complex business questions into scalable, secure, and efficient data engineering solutions.