Data Architect in San Jose

The Senior Cloud Data Architect is a hands-on role responsible for designing, evolving, and optimizing the organization’s cloud-based data architecture. This individual will shape the technical foundation for scalable, secure, and well-governed data systems that power analytics, AI, and enterprise intelligence., As an individual contributor, the architect partners closely with data engineers, analysts, product teams, and cloud specialists to design end-to-end solutions-spanning ingestion, transformation, storage, metadata, and consumption. The ideal candidate brings deep technical expertise in data architecture, metadata design, and cloud- data services, coupled with a keen ability to translate complex requirements into elegant, maintainable designs.

• 4-year Bachelor’s degree in Computer Science (strict requirement). \n

• 7+ years of professional experience in data engineering, architecture, or enterprise analytics platforms, including at least 3+ years focused on cloud data architecture. \n

• Proven experience designing and implementing Azure-based data solutions, including Data Lake, Data Factory, Synapse, and Databricks. \n

• Strong understanding of data modeling, schema design, and metadata management within large-scale data platforms. \n

• Hands-on expertise with distributed data processing frameworks such as Spark and Databricks. \n

• Demonstrated ability to produce and maintain clear architectural documentation and system diagrams. \n

• Proficiency in Python and SQL for pipeline development, data transformation, and automation. \n

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• Architect and optimize cloud-based data lakehouse and warehouse solutions that support analytics, machine learning, and enterprise integration needs. \n

• Define scalable and reusable data frameworks for ingestion, curation, transformation, and consumption. \n

• Evaluate and integrate Azure cloud services (e.g., Databricks, Data Lake, Event Hubs) to deliver high-performance data solutions. \n

• Implement architectural standards that ensure consistency, interoperability, security, and compliance across the data environment. \n

• Partner with engineering and business stakeholders to align architectural decisions with organizational objectives and KPIs. \n

• Drive architectural reviews, proof-of-concepts, and recommendations for future-state cloud data patterns. \n

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Metadata, Governance & Lineage

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• Design and operationalize metadata-driven architectures that improve discoverability, lineage tracking, and data quality monitoring. \n

• Collaborate with governance and engineering teams to implement active metadata approaches, enabling dynamic data cataloging and lineage visibility across pipelines. \n

• Define and enforce standards for metadata capture, schema management, and classification in alignment with enterprise data governance policies. \n

• Integrate data catalog tools and frameworks (e.g., Unity Catalog, Purview, or Collibra) with cloud ecosystems for automated metadata flow. \n

• Ensure consistent application of metadata structures across ingestion, transformation, and consumption layers. \n

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Diagramming, Documentation & Technical Clarity

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• Produce detailed architecture artifacts, including data flow diagrams, system blueprints, and logical/physical data models. \n

• Communicate technical concepts clearly through visualization tools like Lucidchart, Visio, or Draw.io. \n

• Maintain robust documentation of architecture decisions, integration patterns, and system dependencies. \n

• Support cross-functional collaboration by sharing architecture roadmaps and data lineage documentation. \n

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Hands-On Implementation & Optimization

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• Contribute to the design and implementation of distributed data pipelines using Databricks, and Spark \n

• Apply advanced optimization principles for performance, cost, and scalability across compute and storage layers. \n

• Troubleshoot data latency, integrity, and transform issues across multi-environment pipelines. \n

• Implement modernization best practices such as CI/CD automation, schema evolution management, and pipeline observability. \n

• Partner with DevOps and platform teams to ensure maintainability and resilience of deployed solutions. \n

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