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Resident Solution Architect

Capgemini Engineering

Job Title

Resident Solution Architect

Scroll down the page to see all associated job requirements, and any responsibilities successful candidates can expect.

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. xhyhwjd
  • Current knowledge of Databricks products, platform capabilities, and best practices for scalability, reliability, and governance.
Vacancy posted 6 days ago
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