Databricks Architect

• 12-15+ years of experience in Data Engineering, Data Platforms, Data Analytics, and Modern Data Warehouse solutions, with 10+ years of overall consulting and client-facing delivery experience.
• Demonstrated success delivering 6-8+ end-to-end Databricks implementations, serving as a hands-on developer, technical lead, or solution architect.
• Databricks Data Engineering Professional certification (or equivalent advanced Databricks certification) with completion of all recommended learning paths and coursework.
• Databricks has a Databricks Solutions Architect Champion program- this will be good to have
• Strong expertise in designing and implementing cloud-native data platforms across AWS, Azure, and/or Google Cloud Platform, with deep hands-on proficiency in at least one cloud ecosystem.
• Advanced knowledge of Apache Spark, including performance optimization, partitioning strategies, execution plans, memory management, and Spark runtime internals.
• Extensive hands-on experience developing scalable ETL/ELT pipelines using Databricks, Delta Lake, Structured Streaming, and modern data engineering frameworks.
• Experience implementing DevOps and CI/CD practices for production-grade data solutions using tools such as Azure DevOps, GitHub Actions, GitLab CI/CD, or Jenkins.
• Working knowledge of MLOps principles, machine learning lifecycle management, model deployment, and monitoring within enterprise environments.
• Current and broad understanding of the Databricks Lakehouse Platform, including Delta Lake, Unity Catalog, Workflows, MLflow, Delta Live Tables, and other platform capabilities.
• Strong experience tuning large-scale distributed workloads and designing highly performant, scalable, and cost-efficient data processing solutions.

Ability to troubleshoot complex data platform challenges and recommend architecture patterns aligned with business and technical requirements