Data Solutions Architect - AI (W2 Only)

Role: Data Solutions Architect - AI

Location: San Antonio, Texas – Hybrid onsite 4 days a week

Type: Contract – W2 Only

VISA: All work Statuses

Duration: Long Term

*Any Refinery, Oil & gas or Energy domain experience is a big plus.

Please find a much more detailed job description for this opportunity. They are now calling this a Domain Architect vs Solutions Architect.

Job Responsibilities:

The Domain Architect – Data, Analytics & AI provides strategic and technical architecture leadership for a defined business domain, ensuring that data, analytics, and AI solutions are aligned with business priorities, enterprise architecture standards, and the organization's broader data strategy.

This role serves as the architecture authority and primary technology partner for the domain, translating business capabilities and strategic initiatives into scalable data and analytics architectures. The Domain Architect works across business stakeholders, Data Architecture, Data Engineering, Analytics/BI, Data Science & AI, Data Governance, Platform Engineering, and Solution Delivery teams to ensure solutions are integrated, governed, reusable, secure, and designed for long-term value.

The Domain Architect is expected to look beyond individual projects and design toward a cohesive domain architecture, reducing duplication and technical debt while advancing reusable data products, unified data models, modern analytics capabilities, and AI-ready data foundations.

This position belongs to a family of jobs with increasing responsibility, competency, and skill level. Actual position title and pay grade will be based on the selected candidate’s experience

and qualifications.

Skills

Strategic Thinking - Connects business strategy with long-term Data, Analytics & AI capabilities and develops architecture roadmaps that enable future business needs.

Architecture Leadership - Provides clear technical direction, challenges designs constructively, and drives architecture decisions across organizational boundaries.

Business Partnership - Understands business processes and outcomes and translates them into practical technology and data strategies.

Enterprise Mindset - Optimizes for the broader enterprise rather than individual applications or projects, emphasizing reuse, interoperability, standards, and simplification.

Influence & Collaboration - Builds alignment across business, architecture, engineering, analytics, governance, security, and platform teams.

Thought Leadership - Continuously evaluates emerging Data & AI technologies and determines where new capabilities can create measurable enterprise value.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, Engineering, or related discipline, or equivalent professional experience.
• 8+ years of experience across Data Architecture, Solution Architecture, Data Engineering, Analytics Architecture, or related technology disciplines.
• Demonstrated experience designing enterprise-scale data and analytics solutions.
• Strong knowledge of modern data architecture concepts including:
• Data lakes and lakehouse architectures
• Data warehouses
• Data modeling
• Data products and domain-oriented architecture
• ETL/ELT and data integration
• Streaming and event-driven data
• Metadata, lineage, and cataloging
• Data quality and observability
• BI and semantic modeling
• Cloud data platforms
• AI/ML and generative AI architecture concepts
• Experience working with cloud-based enterprise data ecosystems.
• Strong understanding of security, governance, privacy, and regulatory considerations for enterprise data.
• Ability to communicate complex architecture concepts to both technical and executive audiences.

Preferred Technology Experience

• Azure Databricks / Lakehouse Architecture
• Microsoft Azure
• Power BI
• Azure Data Lake Storage
• Azure Data Factory / Synapse
• Data observability and data quality platforms
• Enterprise metadata and lineage solutions
• CI/CD and DevSecOps practices for data platforms
• APIs, event-driven architecture, and enterprise integration
• Generative AI, AI agents, vector search, knowledge graphs, and modern AI platform capabilities

Key Responsibilities

Domain Architecture & Strategy

• Own and maintain the Data & Analytics architecture and technology roadmap for the assigned business domain.
• Develop a deep understanding of the domain's business processes, capabilities, data, applications, analytics needs, and strategic priorities.
• Translate business strategies and capabilities into target-state data, analytics, integration, and AI architectures.
• Establish current-state, transitional, and target-state architectures that guide modernization and investment decisions.
• Identify architectural gaps, redundancies, technical debt, and opportunities for simplification and modernization.
• Ensure individual initiatives contribute toward a cohesive enterprise and domain architecture rather than creating isolated solutions.

Data Architecture & Data Products

• Define and guide the development of domain data models, unified data models, and reusable data products.
• Partner with Data Architects and Data Modelers to review and challenge conceptual, logical, and physical data designs.
• Promote common definitions, reusable data structures, interoperability, and consistent modeling standards across the domain.
• Establish clear domain boundaries, ownership, authoritative data sources, and system-of-record/system-of-reference patterns.
• Drive modernization and decommissioning of redundant or legacy data structures where appropriate.
• Ensure data products are designed for reuse across reporting, analytics, operational use cases, Data Science, and AI.

Analytics, BI & AI Architecture

• Guide architecture for reporting, analytics, advanced analytics, machine learning, generative AI, and agentic AI use cases within the domain.
• Partner with BI and Analytics teams to promote reusable semantic and unified data models rather than report-specific data structures.
• Ensure analytical solutions leverage trusted, governed, and reusable enterprise data.
• Evaluate where AI capabilities can create measurable business value within the domain.
• Ensure data foundations are designed to support both traditional analytics and emerging AI workloads.
• Provide architectural guidance for AI integration, grounding, data access, governance, security, and observability.

Architecture Governance & Solution Reviews

• Serve as the architecture authority for Data & Analytics solutions within the assigned domain.
• Lead and participate in architecture reviews for major initiatives and analytical use cases.
• Review proposed architectures and challenge designs when they introduce unnecessary complexity, duplication, security risks, or technical debt.
• Ensure solutions comply with enterprise architecture principles, approved technology patterns, data standards, security requirements, and governance policies.
• Document architectural decisions, exceptions, risks, dependencies, and technical recommendations.
• Balance enterprise standards with practical business delivery needs and speed-to-value.

Data Governance, Quality & Security

• Embed Data Governance, Data Quality, Security, Privacy, Lineage, and Observability into architecture designs.
• Partner with Data Governance teams to establish domain ownership, stewardship, business definitions, metadata, classifications, and critical data elements.
• Ensure appropriate controls exist for sensitive and regulated information.
• Promote automated data quality monitoring for data both at rest and in transit.
• Ensure architectures provide traceability from source systems through transformation, data products, semantic models, analytics, and downstream consumption.
• Support enterprise security, risk, audit, and regulatory objectives through architecture standards and controls.

Modernization & Technical Debt

• Identify legacy platforms, pipelines, data models, reports, integrations, and technologies that should be modernized or retired.
• Develop architecture roadmaps for moving legacy capabilities toward strategic enterprise platforms and standards.
• Promote consolidation and reuse to reduce duplicate pipelines, datasets, reports, and technology capabilities.
• Partner with engineering and platform teams to establish practical migration and decommissioning strategies.
• Ensure modernization efforts improve scalability, reliability, maintainability, security, and cost efficiency—not simply move existing technical debt to a new platform.

Cross-Functional Leadership

• Act as the architectural bridge between business leadership and technology delivery teams.
• Partner closely with Enterprise Architects, Solution Architects, Data Architects, Security Architects, Data Engineers, Analytics Engineers, Data Scientists, BI Developers, Product Owners, and Data Governance professionals.
• Influence architecture decisions across teams without relying solely on direct organizational authority.
• Facilitate technical discussions and drive teams toward clear architecture decisions when competing approaches exist.
• Mentor architects, engineers, data modelers, and technical leads on architecture principles and modern Data & Analytics practices.
• Champion architectural thought leadership and introduce emerging technologies and patterns where they provide meaningful business value.