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# Databricks Engineer

- **Company:** Mphasis
- **Location:** Columbus, OH, United States
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Amazon Web Services, Microsoft Azure, Cloud Database, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Transformation, Apache Hive, Python (Programming Language), Standard Sql, SQL Databases, Data Logging, Data Processing, Google Cloud, Apache Spark, Caching, Git, Data Lakes, Pyspark, Deployment Automation, Stream Processing, Data Pipelines, Databricks
- **Published:** October 27, 2026
- **Apply:** https://www.dice.com/job-detail/f19ba40e-2705-4922-9b41-234cc535baf0

## About the Role

Strong hands-on experience with Databricks
Strong Python programming skills
Expertise in Apache Spark, PySpark, and Spark SQL- Strong SQL and data transformation skills
Experience with Delta Lake and lakehouse concepts- Knowledge of ETL/ELT and data pipeline design
Experience with AWS, Azure, or Google Cloud Platform- Familiarity with Git, CI/CD, and Agile practices
Good problem-solving and communication skills

## Description

Summary: We are looking for a Databricks Developer with strong experience in Python and Apache Spark to design, develop, and optimize scalable data pipelines and cloud-based data solutions. The ideal candidate will have hands-on expertise in Databricks, PySpark, SQL, and modern data engineering practices., Design, develop, and maintain ETL/ELT pipelines using Databricks and PySpark- Build scalable batch and real-time data processing solutions using Apache Spark- Develop data transformation workflows using Python, PySpark, and Spark SQL- Implement Delta Lake solutions and optimize data processing performance- Create and manage Databricks notebooks, jobs, and workflows- Ensure data quality, validation, logging, and exception handling- Optimize Spark performance using partitioning, caching, and tuning techniques- Collaborate with cross-functional teams to gather requirements and deliver solutions- Support CI/CD, deployment automation, and production releases- Troubleshoot and resolve issues in data pipelines and platform processes