Resident Solution Architect

Job Title : Resident Solution Architect

Location

This is a remote role in the USA.

About the job you're considering

As a Resident Solutions Architect, you will serve as a trusted technical advisor and hands-on leader for enterprise data and analytics initiatives. You will partner with client stakeholders and delivery teams to design, optimize, and scale modern data platforms leveraging Databricks and cloud-native technologies. This role combines deep technical expertise with consulting and architecture leadership to drive successful business outcomes.

Your role

• Lead the design and implementation of scalable data engineering and analytics solutions on Databricks.
• Partner with client and delivery teams to define architecture standards, best practices, and technical roadmaps.
• Provide hands-on guidance for data platform development, optimization, and production deployments.
• Drive performance tuning, scalability improvements, and cost optimization across cloud and Databricks environments.
• Collaborate with engineering teams to establish CI/CD, DevOps, and MLOps best practices.
• Mentor technical teams on distributed computing, Spark architecture, and modern data platform design.
• Evaluate emerging Databricks and cloud capabilities and recommend adoption strategies that align with business goals.

Your skills and experience

• 7+ years of experience in data engineering, data platforms, and analytics.
• Completed Databricks Data Engineering Professional certification and required training curriculum.
• Proven experience delivering 6-8+ successful projects with significant hands-on Databricks development expertise.
• Strong experience with distributed computing using Apache Spark, including understanding of Spark runtime internals and performance optimization techniques.
• Experience designing and deploying solutions across cloud platforms, with deep expertise in AWS, Azure, or GCP and working knowledge of at least one additional cloud ecosystem.
• Familiarity with CI/CD pipelines, production deployment practices, and cloud-native development methodologies.
• Working knowledge of MLOps frameworks, machine learning lifecycle management, and model deployment processes.
• Current knowledge of Databricks products, platform capabilities, and best practices for scalability, reliability, and governance.