Resident Solution Architect

Job Description

Job Title : Resident Solution Architect

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Location

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This is a remote role in the USA.

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About the job you're considering

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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.

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Your role

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

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