Senior Databricks Data Engineer

Responsibilities:

• Responsible for leading the design, development, optimization, and deployment of large-scale robust data solutions and data pipelines within the Databricks Lakehouse platform. This role requires technical leadership, advanced performance tuning expertise and a strong understanding of cloud architecture and DevOps practices.
• Lead the end-to-end design and implementation of scalable data pipelines and a medallion architecture (bronze, silver, gold layers) for structured and unstructured data.
• Architect and implement complex ETL/ELT processes to process and transform large-scale data sets from diverse sources using Databricks notebooks, Delta Lake, and Apache Spark (PySpark, Scala, or SQL)
• Optimize Spark jobs for performance and cost-efficiency using advanced techniques such as partitioning, caching, cluster configuration tuning, and troubleshooting bottlenecks.
• Implement data security strategies, compliance standards, and governance frameworks like Unity Catalog, including managing access controls, encryption, and data lineage.
• Champion code quality and implement DevOps (CI/CD) pipelines using tools like Azure DevOps, GitHub Actions, or Jenkins to automate deployment and management of data assets and infrastructure.
• Develop and implement robust monitoring, logging, and altering systems for production grade pipelines to proactively identify and resolve issues.

Requirements:

• 10+ years of experience in data engineering with 5+ years specifically in Databricks development and leadership roles.
• 5 years of experience in optimizing Spark jobs for performance and cost efficiency using advanced techniques such as partitioning, caching, cluster configuration tuning, and troubleshooting bottlenecks.
• Advanced deep knowledge in Scala, Python, and SQL for optimizing code and build high performance data pipelines on Databricks.
• 5 years of experience in Scala, Spark and Databricks programming.
• 5-10 years of experience implementing complex ETL/ELT processes to process and transform large scale datasets from diverse sources using Databricks notebooks, Delta Lake, and Apache Spark (PySpark, Scala, or SQL).
• Must be able to design and develop CI/CD pipelines that can push the code to different development and prod environments, preferably to Azure using Git Actions.
• Deep knowledge of the Apache Spark ecosystem, Delta Lake and Lakehouse architecture.
• Hands-on experience with Azure and their relevant data services (Azure Data Lake Storage, Azure Data Factory)
• Prefer Databricks Certified Data Engineer Professional certification.

Education:

• Bachelor's degree in Computer Science/Engineering or a related field