Clevanoo LLC
Cloud Data Architect
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Cloud Data Architect in San Jose, CA, with a long-term contract. It requires a Bachelor's degree, 5+ years in data engineering, expertise in Azure and big data technologies, and strong diagramming skills. Must work EST hours.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
December 16, 2025
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
San Jose, CA
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🧠 - Skills detailed
#Data Lake #Data Engineering #Hadoop #Monitoring #Data Bricks #ML (Machine Learning) #Cloud #GDPR (General Data Protection Regulation) #Computer Science #Compliance #Security #Data Security #Data Access #Distributed Computing #Scala #Data Science #Deployment #Databricks #Data Architecture #Scripting #SQL (Structured Query Language) #"ETL (Extract #Transform #Load)" #Strategy #Agile #Documentation #Python #Spark (Apache Spark) #Kubernetes #Big Data #Azure #Automation #Storage #Batch #Database Systems #AI (Artificial Intelligence) #Visualization #Data Ingestion
Role description
Job Title: Cloud Data Architect
Location: San Jose, CA (Day 1 On-Site)
Duration: Long Term
Required Qualifications
· Bachelor's degree in computer science, Data Science, Information Systems, or a related field.
· Minimum of 5 years of hands-on data engineering experience using distributed computing approaches (Spark, Map Reduce, DataBricks)
· Proven track record of successfully designing and implementing cloud-based data solutions in Azure
· Deep understanding of data modelling concepts and techniques.
· Strong proficiency with database systems (relational and non-relational).
· Exceptional diagramming skills with tools like Visio, Lucid chart, or other data visualization software.
Preferred Qualifications
· Advanced knowledge of cloud-specific data services (e.g., Data Bricks, Azure Data Lake).
· Expertise in big data technologies (e.g., Hadoop, Spark).
· Strong understanding of data security and governance principles.
· Experience in scripting languages (Python, SQL).
What you do –
The Cloud Architect will be a key contributor to designing, evolving, and optimizing our company's cloud-based data architecture. This role requires a strong background in data engineering, hands-on experience building cloud data solutions, and a talent for communicating complex designs through clear diagrams and documentation. Must work EST hours.
Strategy, Planning, and Roadmap Development: Align AI and ML system design with broader business objectives, shaping technology roadmaps and architectural standards for end-to-end cloud-driven analytics and AI adoption.
Designing End-to-End AI/ML Workflows: Architect and oversee all stages of AI/ML pipeline development—data ingestion, preprocessing, model training, validation, deployment, monitoring, and lifecycle management within cloud environments.
Selecting Technologies and Services: Evaluate and choose optimal cloud services, AI/ML platforms, infrastructure components (compute, storage, orchestration), frameworks, and tools that fit operational, financial, and security requirements.
Infrastructure Scalability and Optimization: Design and scale distributed cloud solutions capable of supporting real-time and batch processing workloads for AI/ML, leveraging technologies like Kubernetes, managed ML platforms, and hybrid/multi-cloud strategies for optimal performance.
MLOps, Automation, and CI/CD Integration: Implement automated build, test, and deployment pipelines for machine learning models, facilitating continuous delivery, rapid prototyping, and agile transformation for data and AI-driven products.
Security, Compliance, and Governance: Establish robust protocols for data access, privacy, encryption, and regulatory compliance (e.g., GDPR, ethical AI), coordinating with security experts to continuously assess risks and enforce governance.
Business and Technical Collaboration: Serve as the liaison between business stakeholders, development teams, and data scientists, translating company needs into technical solutions, and driving alignment and innovation across departments.
Job Title: Cloud Data Architect
Location: San Jose, CA (Day 1 On-Site)
Duration: Long Term
Required Qualifications
· Bachelor's degree in computer science, Data Science, Information Systems, or a related field.
· Minimum of 5 years of hands-on data engineering experience using distributed computing approaches (Spark, Map Reduce, DataBricks)
· Proven track record of successfully designing and implementing cloud-based data solutions in Azure
· Deep understanding of data modelling concepts and techniques.
· Strong proficiency with database systems (relational and non-relational).
· Exceptional diagramming skills with tools like Visio, Lucid chart, or other data visualization software.
Preferred Qualifications
· Advanced knowledge of cloud-specific data services (e.g., Data Bricks, Azure Data Lake).
· Expertise in big data technologies (e.g., Hadoop, Spark).
· Strong understanding of data security and governance principles.
· Experience in scripting languages (Python, SQL).
What you do –
The Cloud Architect will be a key contributor to designing, evolving, and optimizing our company's cloud-based data architecture. This role requires a strong background in data engineering, hands-on experience building cloud data solutions, and a talent for communicating complex designs through clear diagrams and documentation. Must work EST hours.
Strategy, Planning, and Roadmap Development: Align AI and ML system design with broader business objectives, shaping technology roadmaps and architectural standards for end-to-end cloud-driven analytics and AI adoption.
Designing End-to-End AI/ML Workflows: Architect and oversee all stages of AI/ML pipeline development—data ingestion, preprocessing, model training, validation, deployment, monitoring, and lifecycle management within cloud environments.
Selecting Technologies and Services: Evaluate and choose optimal cloud services, AI/ML platforms, infrastructure components (compute, storage, orchestration), frameworks, and tools that fit operational, financial, and security requirements.
Infrastructure Scalability and Optimization: Design and scale distributed cloud solutions capable of supporting real-time and batch processing workloads for AI/ML, leveraging technologies like Kubernetes, managed ML platforms, and hybrid/multi-cloud strategies for optimal performance.
MLOps, Automation, and CI/CD Integration: Implement automated build, test, and deployment pipelines for machine learning models, facilitating continuous delivery, rapid prototyping, and agile transformation for data and AI-driven products.
Security, Compliance, and Governance: Establish robust protocols for data access, privacy, encryption, and regulatory compliance (e.g., GDPR, ethical AI), coordinating with security experts to continuously assess risks and enforce governance.
Business and Technical Collaboration: Serve as the liaison between business stakeholders, development teams, and data scientists, translating company needs into technical solutions, and driving alignment and innovation across departments.
