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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