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Mid-level data engineer in Bengaluru with broad Databricks skills attracts high applicant competition.
Data engineering skills are transferable, but Databricks/Lakehouse expertise favors analytics-focused companies.
Explicit 2–5 years and specific Databricks/Spark/Unity Catalog requirements raise screening strictness.
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Design, build, and operate end-to-end data pipelines and analytics on Omnissa's Enterprise Data Lakehouse across AWS and Databricks environments.
Develop and maintain interactive dashboards and reports (Tableau/Superset) to support enterprise reporting and AI initiatives.
Implement data governance, quality, and metadata management practices using Unity Catalog and/or Alation, and contribute to AI/GenAI data preparation and engineering workflows.
2-5 years of experience in Data Engineering & Analytics.
Strong proficiency in SQL and Python; hands-on experience with Apache Spark and Databricks ecosystem including Unity Catalog, Delta Live Tables, Workflows, and notebooks.
Experience with AWS data stack components (S3, Glue, Athena, EMR), Apache Airflow, and large dataset processing engines such as Starburst/Trino/AWS Athena.
CI/CD experience with GitHub Actions and dashboarding skills in Tableau, Superset, or PowerBI.
Experienced in working with modern Data Lakehouse architectures using open table formats like Apache Iceberg or Delta Lake.
Comfortable translating complex business questions into scalable, secure data solutions, indicating strong communication and problem-solving skills.
Interested or familiar with AI/GenAI technologies including RAG patterns, embeddings, AI-assisted development, indicating readiness to support AI-powered data initiatives.