Jobs via Dice
Cloud Data Architect
Location:
San Jose, CA (Day 1 On-Site)
Employment Type:
Full-time
Seniority Level:
Mid‑Senior level
Required Qualifications
Bachelor's degree in Computer Science, Data Science, Information Systems, or related field.
Minimum 5 years of hands‑on data engineering experience using distributed computing approaches (Spark, MapReduce, Databricks).
Proven track record of successfully designing and implementing cloud‑based data solutions in Azure.
Deep understanding of data modeling concepts and techniques.
Strong proficiency with database systems (relational and non‑relational).
Exceptional diagramming skills with tools such as Visio, Lucidchart, or other data visualization software.
Preferred Qualifications
Advanced knowledge of cloud‑specific data services (e.g., Databricks, 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
Key contributor to designing, evolving, and optimizing the company’s cloud‑based data architecture.
Strategy, planning, and roadmap development: align AI/ML system design with business objectives, shape 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 the pipeline (data ingestion, preprocessing, model training, validation, deployment, monitoring, lifecycle management) within cloud environments.
Selecting technologies and services: evaluate and choose optimal cloud services, AI/ML platforms, infrastructure components, 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.
MLOps, automation, and CI/CD integration: implement automated build, test, and deployment pipelines for machine learning models, enabling continuous delivery and rapid prototyping.
Security, compliance, and governance: establish robust protocols for data access, privacy, encryption, and regulatory compliance (e.g., GDPR, ethical AI).
Business and technical collaboration: serve as liaison between business stakeholders, development teams, and data scientists, translating needs into technical solutions and driving alignment across departments.
Must work EST hours.
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San Jose, CA (Day 1 On-Site)
Employment Type:
Full-time
Seniority Level:
Mid‑Senior level
Required Qualifications
Bachelor's degree in Computer Science, Data Science, Information Systems, or related field.
Minimum 5 years of hands‑on data engineering experience using distributed computing approaches (Spark, MapReduce, Databricks).
Proven track record of successfully designing and implementing cloud‑based data solutions in Azure.
Deep understanding of data modeling concepts and techniques.
Strong proficiency with database systems (relational and non‑relational).
Exceptional diagramming skills with tools such as Visio, Lucidchart, or other data visualization software.
Preferred Qualifications
Advanced knowledge of cloud‑specific data services (e.g., Databricks, 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
Key contributor to designing, evolving, and optimizing the company’s cloud‑based data architecture.
Strategy, planning, and roadmap development: align AI/ML system design with business objectives, shape 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 the pipeline (data ingestion, preprocessing, model training, validation, deployment, monitoring, lifecycle management) within cloud environments.
Selecting technologies and services: evaluate and choose optimal cloud services, AI/ML platforms, infrastructure components, 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.
MLOps, automation, and CI/CD integration: implement automated build, test, and deployment pipelines for machine learning models, enabling continuous delivery and rapid prototyping.
Security, compliance, and governance: establish robust protocols for data access, privacy, encryption, and regulatory compliance (e.g., GDPR, ethical AI).
Business and technical collaboration: serve as liaison between business stakeholders, development teams, and data scientists, translating needs into technical solutions and driving alignment across departments.
Must work EST hours.
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