Job Description
- Architect, administer, and optimize Databricks workspaces, clusters, jobs, and workflows.
- Design and develop scalable Azure-based data engineering solutions, including Data Lake, Data Factory, Synapse, Key Vault, etc.
- Build high-performance data pipelines using Python and PySpark.
- Work with Hadoop-based platforms and distributed storage systems.
- Implement and manage real-time data streaming solutions and ingestion pipelines.
- Ensure proper configuration, monitoring, governance, and performance tuning of data platforms.
- Integrate CI/CD pipelines on Azure and work with containerization technologies (Docker/K8s).
- Develop and optimize SQL queries, stored procedures, and data models using MS SQL.
- Collaborate with architects, data scientists, and engineering teams to deliver enterprise-grade solutions.
- Define best practices, coding standards, and data engineering frameworks across the organization.
- Troubleshoot complex data issues and ensure high availability of mission-critical data systems.
Requirements
- Minimum 10+ years of total IT experience.
- Minimum 6+ years strong experience in the following areas: Databricks Administration , Azure Cloud , Python & PySpark , Hadoop ecosystem , Data streaming & data pipeline engineering , MS SQL
- Proven experience architecting large-scale distributed data systems.
- Strong understanding of data lakes, data warehousing, and big data architecture patterns.
Nice-to-Have Skills
- Experience with Apache Spark optimization and tuning.
- Hands-on experience with CI/CD pipelines on Azure (GitHub Actions, Azure DevOps, etc.).
- Knowledge of containers (Docker, Kubernetes).
- Understanding of MLOps or advanced analytics integrations.
Originally posted on Himalayas