Senior Databricks Solution Architect - Lakehouse & Data Engineering | Contract | Remote (PST)

We are seeking an experienced Senior Databricks Solution Architect with a strong combination of enterprise solution architecture, hands-on data engineering, and client-facing consulting experience. The ideal candidate will have successfully designed, implemented, and optimized multiple enterprise-scale Databricks solutions, with deep expertise in Apache Spark, Delta Lake, Lakehouse architecture, and cloud-native data platforms. This role requires someone who can lead architecture discussions while remaining actively involved in technical design, development, performance tuning, and troubleshooting., * Design and implement scalable, secure, and cost-efficient enterprise data platforms using the Databricks Lakehouse Platform.

• Lead end-to-end Databricks implementations, from architecture and solution design through development, deployment, and production optimization.
• Develop and optimize scalable ETL/ELT pipelines using PySpark, Apache Spark, Delta Lake, and Structured Streaming.
• Design cloud-native data solutions across AWS, Microsoft Azure, and/or Google Cloud Platform.
• Optimize distributed data processing through partitioning strategies, execution-plan analysis, memory management, and performance tuning.
• Implement platform capabilities including Unity Catalog, Databricks Workflows, Delta Live Tables/Lakeflow, and MLflow.
• Establish DevOps and CI/CD practices using Azure DevOps, GitHub Actions, GitLab CI/CD, Jenkins, or similar tools.
• Apply data governance, security, monitoring, reliability, and cost-optimization best practices.
• Provide hands-on technical leadership, troubleshoot complex platform challenges, and recommend appropriate architecture patterns.
• Lead client workshops, communicate architectural decisions, and translate business requirements into technical solutions.

• 12-15+ years of experience in data engineering, data platforms, data analytics, or modern data warehouse solutions.
• 10+ years of consulting, client-facing delivery, or enterprise technology implementation experience.
• Demonstrated success delivering multiple end-to-end Databricks implementations, ideally 6-8 or more.
• Strong hands-on proficiency in at least one major cloud platform: AWS, Azure, or Google Cloud Platform.
• Advanced knowledge of Apache Spark, PySpark, Spark SQL, distributed computing, and performance optimization.
• Extensive experience with Databricks, Delta Lake, scalable ETL/ELT pipelines, and modern data engineering frameworks.
• Strong understanding of Lakehouse architecture, Unity Catalog, workflow orchestration, and production-grade data platform design.
• Experience with CI/CD, DevOps, production monitoring, and deployment automation.
• Working knowledge of MLOps, MLflow, machine learning lifecycle management, and model monitoring.
• Excellent analytical, troubleshooting, communication, and technical leadership skills.

Preferred Qualifications

• Databricks Certified Data Engineer Professional certification or an equivalent advanced Databricks credential.
• Databricks Solutions Architect Champion recognition.
• Experience in public sector, banking/financial services, or healthcare environments.
• Experience with large-scale platform modernization, data governance, and enterprise cloud migrations.