Databricks Engineer

About Durapid Technologies:- Durapid Technologies is a fast-growing technology company specializing in AI Software Development, Data & AI Solutions, Enterprise Applications, and IT Staffing Services. As a Microsoft Data & AI Solutions Partner, we help organizations build scalable data platforms and intelligent business solutions that drive digital transformation. Job Summary:- We are looking for a highly skilled Databricks Engineer with 5-10 years of experience in Data Engineering and Big Data technologies. The ideal candidate should have strong hands-on expertise in Databricks, PySpark, SQL, and Streaming Technologies, with experience building enterprise-grade data pipelines and processing large-scale datasets in both batch and real-time environments. Key Responsibilities:- Design, develop, and maintain scalable data pipelines using Databricks and PySpark. Build and optimize batch and real-time data processing solutions using modern data engineering practices. Develop complex SQL queries, data transformations, and ETL/ELT workflows. Work with large-scale datasets and streaming technologies such as Kafka and Spark Streaming. Implement data quality checks, monitoring mechanisms, and performance optimization strategies. Collaborate with business, analytics, and engineering teams to deliver data-driven solutions. Ensure reliability, scalability, and maintainability of enterprise data platforms. Required Qualifications:- 5-8 years of overall IT experience with strong Data Engineering expertise. Hands-on experience working with Databricks in production environments. Strong experience in designing and optimizing data pipelines. Excellent SQL skills including complex queries, transformations, and stored procedures. Experience working with large-scale datasets and distributed processing frameworks. Databricks Certification is mandatory. Must be willing to work from the Jaipur office. Strong communication and stakeholder management skills. Mandatory Skills:- Databricks ( Certification Mandatory ). PySpark. Streaming Technologies (Kafka, Spark Streaming, Structured Streaming, etc.). SQL & Data Pipeline Development. Batch & Real-Time Data Processing. Data Quality, Reliability, and Performance Optimization. Azure Data Factory. Good to Have:- Delta Lake and Medallion Architecture. Azure, AWS, or GCP exposure. CI/CD and DevOps practices for Data Engineering. Data Governance and Data Quality Frameworks.