Senior Databricks Engineer - Python/SQL/Scala

Job Description

About the job : - As a Mid Databricks Engineer, you will play a pivotal role in designing, implementing, and optimizing data processing pipelines and analytics solutions on the Databricks platform. - You will collaborate closely with cross-functional teams to understand business requirements, architect scalable solutions, and ensure the reliability and performance of our data infrastructure. - This role requires deep expertise in Databricks, strong programming skills, and a passion for solving complex engineering challenges. What You'll Do : - Design and develop data processing pipelines and analytics solutions using Databricks. - Architect scalable and efficient data models and storage solutions on the Databricks platform. - Collaborate with architects and other teams to migrate current solution to use Databricks. - Optimize performance and reliability of Databricks clusters and jobs to meet SLAs and business requirements. - Use best practices for data governance, security, and compliance on the Databricks platform. - Mentor junior engineers and provide technical guidance. - Stay current with emerging technologies and trends in data engineering and analytics to drive continuous improvement. You'll Be Expected To Have : - Bachelor's or Master's degree in Computer Science, Engineering, or a related field. - 3 to 6 years of overall experience and 2+ years of experience designing and implementing data solutions on the Databricks platform. - Proficiency in programming languages such as Python, Scala, or SQL. - Strong understanding of distributed computing principles and experience with big data technologies such as Apache Spark. - Experience with cloud platforms such as AWS, Azure, or GCP, and their associated data services. - Proven track record of delivering scalable and reliable data solutions in a fast-paced environment. - Excellent problem-solving skills and attention to detail. - Strong communication and collaboration skills with the ability to work effectively in cross-functional teams. - Good to have experience with containerization technologies such as Docker and Kubernetes. - Knowledge of DevOps practices for automated deployment and monitoring of data pipelines.