This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Resident Solution Architect (Databricks) based in United States.

This is a senior, hands-on architecture role focused on delivering large-scale Databricks implementations for enterprise clients.
You’ll operate at the intersection of data engineering, cloud architecture, and client advisory, shaping modern Lakehouse environments from design through production.
You’ll help organizations make strategic platform decisions while also getting deeply involved in technical delivery and optimization.
The role offers the opportunity to work on complex, production-grade data infrastructure across sophisticated and regulated environments.
You’ll tackle distributed computing, performance optimization, CI/CD, and MLOps challenges at significant scale.
Working directly with enterprise stakeholders, you’ll translate business and technical needs into scalable, resilient data platforms.
This is an ideal opportunity for an experienced architect who enjoys combining technical depth, client engagement, and hands-on execution.

• Lead end-to-end Databricks implementation initiatives, taking solutions from architecture and design through deployment and production.
• Advise enterprise clients on Lakehouse architecture, platform capabilities, best practices, technology roadmaps, and implementation strategies.
• Design scalable and resilient data platforms capable of supporting complex enterprise workloads and evolving business requirements.
• Optimize large-scale distributed data systems, identifying and resolving performance and scalability challenges across production environments.
• Serve as a hands-on technical lead, contributing directly to cloud-based implementations across AWS, Azure, and/or GCP.
• Design, implement, and support CI/CD pipelines that enable reliable and repeatable production deployments for data platforms.
• Apply MLOps principles and patterns to connect data engineering workflows with machine learning development and deployment processes.
• Collaborate closely with client technical teams and stakeholders, providing architectural guidance and helping drive successful adoption of modern Lakehouse practices.
• Bring practical expertise from multiple enterprise implementations to solve complex technical challenges and establish scalable architectural patterns.

• 10+ years of consulting experience, including at least 7 years focused on data engineering, data platforms, analytics, or closely related disciplines.
• Proven hands-on delivery of approximately 6–8 or more enterprise-scale Databricks implementation projects.
• Deep expertise in Apache Spark and distributed computing, including a strong understanding of Spark runtime internals.
• Extensive knowledge of the Databricks Lakehouse Platform, its capabilities, architectural patterns, and current best practices.
• Databricks Data Engineering Professional Certification is strongly preferred.
• Demonstrated experience optimizing and tuning large-scale data platforms for performance, reliability, and scalability.
• Hands-on experience with at least one major cloud platform, including AWS, Azure, or GCP; multi-cloud experience is an advantage.
• Solid understanding of CI/CD practices and pipelines for production data-platform deployments.
• Working knowledge of MLOps concepts and their application across modern data and machine learning workflows.
• Strong consulting, communication, and client-facing skills, with the ability to explain complex technical concepts and influence architectural decisions.
• Ability to work independently in hands-on technical environments while balancing delivery priorities and client advisory responsibilities.
• Must be authorized to work in the United States; visa sponsorship is not available for this position.

• Compensation of up to $80/hour on W2.
• Fully remote position with flexibility to work from anywhere in North America.
• Opportunity to lead enterprise-scale Databricks and Lakehouse transformations.
• Exposure to complex, high-impact data environments and modern cloud technologies.
• Hands-on work across data engineering, distributed computing, CI/CD, and MLOps.
• Senior-level client advisory and technical leadership opportunities.
• Opportunity to shape production-grade data infrastructure and influence enterprise technology roadmaps.