Resident Solution Architect, Databricks

Clera

Resident Solution Architect delivering Databricks Lakehouse implementations for an IT and MarTech consultancy. Advising enterprise clients on data engineering, analytics, cloud platforms, and MLOps.

Posted 8/5/2026full-timeRemote • California • 🇺🇸 United StatesSeniorLead💰 $80 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in Databricks implementations, including the Databricks Lakehouse Platform and Apache Spark for optimizing large-scale data platforms. Proficient in designing CI/CD pipelines and MLOps workflows while delivering cloud-native solutions across AWS, Azure, and GCP.

Highest-signal resume keywords

Databricks Lakehouse PlatformApache Spark ExpertiseCI/CD Pipeline DesignMLOps WorkflowsCloud Platform Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData PlatformsAnalyticsPerformance TuningOptimizationScalabilityDatabricks ImplementationDistributed ComputingCloud-Native SolutionsArchitectural Recommendations

Soft Skills

Stakeholder CommunicationTeam GuidanceConsulting Experience

Tools & Technologies

AWSAzureGCPDatabricksDelta LakeCI/CD ToolsMLOps Tools

Certifications & Qualifications

Databricks Data Engineering Professional Certification

Industry Keywords

Enterprise ClientsData Platform BuildsCloud SolutionsMulti-Cloud Experience

Tech Stack

Tools & technologies

ApacheAWSAzureCloudGoogle Cloud PlatformSpark

About the role

Key responsibilities & impact

Architect, design, and deliver end-to-end Databricks implementations for enterprise clients
Serve as a hands-on solution architect, guiding technical teams through complex data platform builds
Apply Databricks Lakehouse Platform capabilities, Delta Lake, and best practices to drive client outcomes
Leverage Apache Spark expertise, including runtime internals, for performance tuning, optimization, and scalability of large-scale data platforms
Design and support CI/CD pipelines for production data platform deployments
Advise on and implement MLOps workflows with data science teams
Work across AWS, Azure, or GCP to deliver scalable, cloud-native solutions
Translate complex technical requirements into architectural recommendations for technical and non-technical stakeholders

Requirements

What you’ll need

Must be authorized to work in the United States without visa sponsorship; no C2C or 1099
10+ years of consulting experience
7+ years focused on Data Engineering, Data Platforms, and Analytics
Hands-on delivery of 6–8+ enterprise-scale Databricks implementation projects
Strong understanding of the Databricks Lakehouse Platform, including capabilities and best practices
Deep expertise in Apache Spark and distributed computing, including Spark runtime internals
Completed Databricks Data Engineering Professional Certification
Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP
Proven experience with performance tuning, optimization, and scalability of large-scale data platforms
Solid understanding of CI/CD pipelines for production deployments
Working knowledge of MLOps principles and tooling
Multi-cloud experience spanning two or more of AWS, Azure, and GCP is nice to have

Benefits

Comp & perks

Up to $80/hr on W2
W2 contract engagement