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