Data Solutions Architect

Role: Data Solutions Architect

Employment Type: Contract

City: Glen Allen

State: Virginia

Looking for strong experience in enterprise data architecture, cloud platforms, governance, and data modernization, hands-on experience with GenAI platforms, LLM-based solution architecture, RAG, MCP integrations, AI governance, agentic workflows, or enterprise AI adoption and value realization programs.

Description:

The ideal candidate combines deep expertise in data architecture, cloud platforms, AI technologies, and enterprise integration patterns with strong leadership and communication skills. This individual serves as a strategic advisor and technical leader, ensuring technology investments aligning with business priorities while establishing scalable architectural standards and governance practices.

The successful candidate is expected to actively participate in solution design, architecture validation, proof-of-concept development, technical troubleshooting, and implementation activities. The architect must be comfortable rolling up their sleeves, working alongside engineers, reviewing code and design patterns, validating technical approaches, and helping teams deliver production-ready solutions. Success in this role requires balancing strategic architecture leadership with meaningful hands-on technical execution.

Key Responsibilities:

Functional Responsibilities

· Design end-to-end enterprise data and AI solution architectures.

· Define architecture patterns for data ingestion, transformation, storage, analytics, machine learning, and generative AI use cases.

· Establish scalable and reusable architecture standards across platforms and technologies.

· Lead technical evaluations and recommendations for data, AI, and integration technologies.

· Actively contribute to the design and implementation of enterprise data and AI solutions.

· Develop prototypes, proofs of concept, and reference implementations to validate architectural approaches and emerging technologies.

· Partner directly with engineering teams to solve complex technical challenges and remove delivery roadblocks.

· Perform hands-on architecture validation, solution reviews, and technical troubleshooting throughout the software delivery lifecycle.

· Provide architectural leadership through delivery, demonstrating best practices in cloud platforms, data engineering, integration, automation, AI, and DevOps.

· Maintain current hands-on expertise with modern data and AI technologies including Snowflake, Azure, Databricks, Microsoft Fabric, AI platforms, and integration frameworks.

· Provide architectural oversight for strategic initiatives and major technology investments.

· Actively lead design reviews and ensure adherence to enterprise architecture standards.

· Collaborate with engineering teams to translate architectural designs into implementable solutions.

Governance, Risk & Compliance

· Establish architectural governance frameworks for data and AI solutions.

· Ensure alignment with privacy, security, regulatory, and compliance requirements.

· Define standards for data lineage, metadata management, model governance, and AI transparency.

· Identify and mitigate architectural, operational, and cybersecurity risks.

· Maintain architecture documentation, reference models, and technical standards.

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Reporting & Communication

· Develop executive-level architecture presentations and recommendations.

· Communicate technical concepts effectively to both technical and non-technical audiences.

· Present architecture roadmaps, risks, trade-offs, and investment recommendations.

· Provide regular updates on strategic initiatives, architecture reviews, and innovation opportunities.

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Success Measures

· Establish enterprise architecture standards for data and AI solutions.

· Deliver scalable architecture designs supporting strategic business initiatives.

· Accelerate delivery of enterprise data and AI capabilities through practical architecture guidance and hands-on problem solving.

· Create an execute roadmap for AI-based self-serve analytics

· Increase adoption of enterprise data platforms and AI capabilities.

· Improve consistency and governance across data and analytics ecosystems.

· Demonstrate measurable business value from AI and advanced analytics initiatives.

· Reduce architectural complexity through standardization and modernization efforts.

· Build credibility with engineering teams through technical leadership and direct contribution to successful solution delivery.

Requirements

Education

· Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or related field.

· Equivalent combination of education and experience may be considered.

Experience

· 10 years of experience in data architecture, enterprise architecture, analytics platforms, or software engineering.

· 3 years of experience designing AI, machine learning, or advanced analytics solutions.

· Experience defining Enterprise AI strategies and governance programs.

· Experience with Generative AI, LLMs, RAG architectures, AI agents, and AI orchestration frameworks.

· Demonstrated success leading complex enterprise technology initiatives.

· Experience working across multiple business functions and stakeholder groups.

· Experience with cloud-native architecture and modern data platforms.

· Experience leading large-scale cloud transformation initiatives.

Preferred Qualifications

· Master's degree in Computer Science, Data Science, Artificial Intelligence, Information Systems, or related field.

· Experience in healthcare, distribution, supply chain, or regulated industries.

Preferred Certifications

· Snowflake SnowPro Advanced Certification

· Microsoft Azure Solutions Architect Expert

· Microsoft Azure AI Engineer Associate

· Databricks Data Engineer or Data Architect Certification

· AWS Certified Solutions Architect

· TOGAF Certification

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Technical Skills

Required

· Data Architecture and Modeling

· Data Warehousing and Lakehouse Architecture

· Data Integration and ETL/ELT Architecture

· Cloud Platforms (Azure preferred)

· Snowflake

· Candidates should have hands-on experience with at least one or more: (Claude Enterprise, OpenAI / Azure OpenAI, Cortex)

· Microsoft Fabric

· Sematic Model creation

· MLOps and AI Governance Platforms

· Power BI

· Master Data Management

· API and Event-Driven Architectures

· Data Governance and Metadata Management

· Python

· DevOps and CI/CD

· Informatica IDMC

· SQL and Data Engineering Concepts

· Enterprise Solution Architecture

· AI and Machine Learning Fundamentals

Preferred

· Databricks

· Vector Databases and Semantic Search

· RAG Architecture Design

· Knowledge Graphs