Resident Solution Architect; Databricks

At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential. From autonomous cars to life‑saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities.

Where you can make a difference. Where no two days are the same.

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.

Remote

• 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, Dev Ops, 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.

• 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.

The base compensation range for this role in the posted location is: $73,150 - $174,000

Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.

The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction. These may include, but are not limited to:
Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.

It is not typical for candidates to be hired at or near the top of the posted compensation range. In addition…