Data Engineer (Architect)

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