Solutions Architect Databricks

Role name: Resident Solutions Architect (Databricks)

Work Location: United states of America (remote)

Start date: Immediate availability.

Employment Type: Contract

Interview Mode: Phone/Video

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 Google Cloud Platform 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.