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.
- 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.
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Your skills and experience
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- 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.
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