Data Architect

Role: Data Architect Location: San Jose, CA – Onsite Rate: $65/hr on W2 without benefits Rate: $75/hr on C2C All Inclusive – Only with own corporations consultants Contract role Job description: Data and Cloud Solutions Architect Overview 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-native data services, coupled with a keen ability to translate complex requirements into elegant, maintainable designs. Core Responsibilities Cloud Data Architecture & Strategy • Architect and optimize cloud-based data lakehouse and warehouse solutions that support analytics, machine learning, and enterprise integration needs. • Define scalable and reusable data frameworks for ingestion, curation, transformation, and consumption. • Evaluate and integrate Azure cloud services (e.g., Databricks, Data Lake, Event Hubs) to deliver high-performance data solutions. • Implement architectural standards that ensure consistency, interoperability, security, and compliance across the data environment. • Partner with engineering and business stakeholders to align architectural decisions with organizational objectives and KPIs. • Drive architectural reviews, proof-of-concepts, and recommendations for future-state cloud data patterns. Metadata, Governance & Lineage • Design and operationalize metadata-driven architectures that improve discoverability, lineage tracking, and data quality monitoring. • Collaborate with governance and engineering teams to implement active metadata approaches, enabling dynamic data cataloging and lineage visibility across pipelines. • Define and enforce standards for metadata capture, schema management, and classification in alignment with enterprise data governance policies. • Integrate data catalog tools and frameworks (e.g., Unity Catalog, Purview, or Collibra) with cloud ecosystems for automated metadata flow. • Ensure consistent application of metadata structures across ingestion, transformation, and consumption layers. Diagramming, Documentation & Technical Clarity • Produce detailed architecture artifacts, including data flow diagrams, system blueprints, and logical/physical data models. • Communicate technical concepts clearly through visualization tools like Lucidchart, Visio, or Draw.io. • Maintain robust documentation of architecture decisions, integration patterns, and system dependencies. • Support cross-functional collaboration by sharing architecture roadmaps and data lineage documentation. Hands-On Implementation & Optimization • Contribute to the design and implementation of distributed data pipelines using Databricks, and Spark • Apply advanced optimization principles for performance, cost, and scalability across compute and storage layers. • Troubleshoot data latency, integrity, and transform issues across multi-environment pipelines. • Implement modernization best practices such as CI/CD automation, schema evolution management, and pipeline observability. • Partner with DevOps and platform teams to ensure maintainability and resilience of deployed solutions. Qualifications • 4-year Bachelor’s degree in Computer Science (strict requirement). • 7+ years of professional experience in data engineering, architecture, or enterprise analytics platforms, including at least 3+ years focused on cloud data architecture. • Proven experience designing and implementing Azure-based data solutions, including Data Lake, Data Factory, Synapse, and Databricks. • Strong understanding of data modeling, schema design, and metadata management within large-scale data platforms. • Hands-on expertise with distributed data processing frameworks such as Spark and Databricks. • Demonstrated ability to produce and maintain clear architectural documentation and system diagrams. • Proficiency in Python and SQL for pipeline development, data transformation, and automation. Email: roopesh@lorventech.com