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Mid-level (2–5yr) Databricks data engineer in Bengaluru with a common title and metro location increases competition.
Core data engineering skills transfer across industries, though Databricks/Lakehouse specifics raise domain sensitivity.
Explicit 2–5 years plus mandatory Databricks, Spark, SQL, Airflow, and Unity Catalog make filters strict.
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Design, build, and operate end-to-end data pipelines on Omnissa's Lakehouse architecture across AWS and Databricks platforms.
Develop interactive dashboards and reports (Tableau/Superset) for technical and business stakeholders to support enterprise reporting and AI initiatives.
Implement data governance, orchestration (Apache Airflow, GitHub Actions CI/CD), and optimize data modeling using open table formats like Apache Iceberg and Delta Lake.
2–5 years of experience in Data Engineering and Analytics.
Strong proficiency in SQL and Python; Hands-on experience with Databricks (Unity Catalog, Delta Live Tables, Workflows, notebooks) and Apache Spark for pipeline development.
Experience with AWS data stack (S3, Glue, Athena, EMR), federated query engines (Starburst, Trino, AWS Athena), and Apache Airflow.
Dashboarding experience in Tableau/Superset/PowerBI and CI/CD experience using GitHub Actions.
Experience working with modern Data Lakehouse platforms, focusing on scalability, performance, and secure data engineering practices.
Ability to translate complex business questions into effective data solutions in high-growth, fast-paced environments.
Interest or familiarity with GenAI/LLM concepts (RAG, embeddings, agentic frameworks) and AI-assisted software development tools.