1 month ago
Bengaluru, IndiaMid Level / Senior
Responsibilities
- Build and operate end-to-end data pipelines across AWS and Databricks.
- Develop scalable ingestion, transformation, serving, and data modeling solutions for enterprise reporting and product telemetry.
- Create interactive dashboards and reports in Tableau or Superset for technical and business audiences.
- Write secure, efficient SQL and Python for large datasets using Databricks Spark, Starburst, Trino, and AWS Athena.
- Manage Apache Iceberg and Delta Lake table formats while optimizing cost and performance.
- Orchestrate workflows with Apache Airflow and Databricks Workflows.
- Deliver changes through GitHub Actions CI/CD.
- Apply data governance, quality, cataloging, lineage, and access-policy practices using Unity Catalog and/or Alation.
- Partner with analysts, product teams, and business stakeholders to translate business questions into data solutions.
- Prepare data foundations for RAG and agentic AI use cases and adopt AI-assisted engineering practices.
Requirements
- 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 or declarative pipelines, Workflows, and notebooks.
- Hands-on Apache Spark experience for pipeline development.
- Experience processing large datasets with Starburst, Trino, or AWS Athena.
- Exposure to the AWS data stack, including S3, Glue, Athena, and EMR.
- Experience with Apache Airflow.
- Familiarity with Apache Iceberg or other table formats such as Delta Lake or Hudi.
- Experience with CI/CD and GitHub Actions.
- Dashboarding experience with Tableau, Superset, or Power BI.
- Strong communication skills and the ability to translate business questions into data solutions.
- GenAI or LLM awareness, including RAG patterns, embeddings, vector stores, agentic frameworks, or MCP-based tooling, is preferred.
- Familiarity with AI-assisted development tools such as coding copilots or Claude Code is preferred.
- Comfort with Agile and DevOps ways of working is preferred.
- Strong analytical, problem-solving, communication, ownership, scalability, performance, security, and quality orientation.
Tech Stack
Categories
Data Engineering
About Omnissa
Omnissa builds an enterprise digital work platform that unifies endpoint management, virtual desktops and apps, digital employee experience, and security to manage devices and access across clouds. It sells software and cloud services to IT teams in enterprises, education, and public sector. The company was spun out of VMware’s End‑User Computing business, is owned by KKR, and is headquartered in Mountain View, California.