Resident Solution Architect (RSA) - Databricks

Role: Resident Solutions Architect (RSA) – Databricks

Location: Remote – US
Duration: 12 Months

Role Overview:

We are seeking an experienced Resident Solutions Architect – Databricks to provide technical leadership for enterprise-scale data engineering and analytics initiatives. The ideal candidate will work closely with client stakeholders and engineering teams to architect, implement, optimize, and scale modern data platforms using Databricks and cloud technologies.

Key Responsibilities:

• Architect and deliver scalable data engineering, analytics, and data platform solutions using Databricks.
• Work directly with business, client, and technical stakeholders to establish architecture standards, technical strategies, best practices, and implementation roadmaps.
• Provide hands-on technical direction throughout data platform development, integration, optimization, testing, and production deployment.
• Identify opportunities to improve platform performance, scalability, reliability, and cloud/Databricks cost efficiency.
• Guide engineering teams in implementing CI/CD, DevOps, automation, and MLOps practices for enterprise data solutions.
• Provide technical mentorship on Apache Spark, distributed processing, Spark architecture, and performance optimization.
• Assess new Databricks capabilities and cloud-native technologies and recommend solutions that support business and technical objectives.
• Partner with delivery teams to troubleshoot complex platform and production issues and provide architectural solutions.
• Establish scalable approaches for data governance, security, reliability, and operational excellence across Databricks environments.

Required Qualifications:

• 7+ years of professional experience in data engineering, enterprise data platforms, analytics, or related areas.
• Databricks Data Engineering Professional certification along with completion of the required Databricks training curriculum.
• Demonstrated delivery of 6–8+ successful data projects, with substantial hands-on experience developing solutions within Databricks.
• Strong expertise in Apache Spark and distributed computing, including Spark runtime architecture, troubleshooting, and performance tuning.
• Proven ability to design and deploy modern data platforms across AWS, Azure, or Google Cloud Platform, with practical working knowledge of at least one additional cloud platform.
• Experience with CI/CD pipelines, DevOps practices, automated deployments, and production-grade cloud development.
• Working knowledge of MLOps, machine learning lifecycle processes, model deployment, and operationalization of ML solutions.
• Strong understanding of current Databricks platform capabilities, architecture patterns, scalability, reliability, governance, and enterprise best practices.
• Excellent communication and stakeholder-management skills, with the ability to operate as a trusted technical advisor for enterprise clients.