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Mid-level generalist data role in metro Bengaluru with common toolset drives moderate applicant competition.
Core data engineering skills are transferable, though Databricks/Lakehouse experience moderately narrows opportunities.
Explicit 2–5 year requirement plus mandatory Databricks, Spark, SQL, Python, Airflow skills increases screening strictness.
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Build and operate end-to-end data pipelines on Omnissa's Enterprise Data Lakehouse using AWS and Databricks technologies.
Design and develop interactive dashboards and reports for technical and business users using Tableau or Superset.
Support AI initiatives by preparing data foundations and adopting AI-assisted engineering in delivery processes.
2–5 years of experience in Data Engineering & Analytics.
Strong proficiency in SQL and Python with hands-on experience in Apache Spark and Databricks (Unity Catalog, Delta Live Tables, Workflows).
Experience with AWS data stack components (S3, Glue, Athena, EMR), Apache Airflow, and federated query engines like Starburst/Trino/AWS Athena.
Experience with CI/CD tools such as GitHub Actions and dashboarding tools like Tableau or Superset.
Experienced individual contributor comfortable working independently and managing multiple priorities in a fast-paced environment.
Demonstrates ownership mindset focusing on scalable, performant, secure, and high-quality code design for modern data lakehouse platforms.
Interest and some awareness of GenAI/LLM technologies and their integration with data engineering workloads to support AI-driven use cases.