Data & AI Solution Architect

Indigo Beam•Houston, TX

About The Position

This is a Solution Architect role requiring strong Data Architecture experience. The successful candidate owns end-to-end solution architecture and artefacts, personally develops solution logical data architecture, and guides physical data design. The role delivers architecture for projects within a major energy client, including solutions supporting data, analytics, AI, GenAI, and agent-based use cases. It translates business goals and requirements into fit-for-purpose solution designs aligned with business goals, technology direction, enterprise architecture principles, and client standards. The architect independently produces high-quality architecture artefacts, including solution designs, Solution Logical Data Models, and Physical Data Models where required. They evaluate solution options and trade-offs, considering value, affordability, feasibility, and absorbability; identify architecture risks and dependencies; and provide architecture direction and assurance through implementation. This is primarily an individual-contributor role. The architect owns assigned project and solution deliverables through delivery while working to the direction and priorities set by the Lead Architect.

Requirements

Experience architecting data-intensive solutions that enable analytics, AI, and GenAI use cases, including the data, integration, security, and platform architecture needed to operationalize them.
Experience with structured solution optioning, architecture decision-making, and architecture assurance.
Strong practical experience in Data Architecture and Data Modeling, including Solution Logical Data Models and Physical Data Models.
Strong understanding of modern data architectures, integration patterns, and platforms such as Azure and Databricks.
Ability to balance business value, strategic alignment, delivery feasibility, technical quality, and risk.
Strong stakeholder management and communication skills, including the ability to explain complex architecture decisions clearly.

Nice To Haves

Experience delivering architecture in Upstream, industrial, asset-intensive, or operational environments is strongly preferred.
Experience integrating operational/OT, engineering, historian/time-series, and enterprise IT data is an advantage.

Responsibilities

Develop and maintain end-to-end solution and data architectures across applications, integrations, AI, and data platforms to support analytics, AI, GenAI, and agent-based use cases.
Translate business and functional requirements into implementable architecture designs, evaluating solution options, trade-offs, risks, and dependencies.
Produce and maintain solution context and architecture overviews; solution and application component architecture and interaction views; data architecture and data-flow diagrams; interface, API, and integration views; and architecture decisions, risks, assumptions, and dependencies.
Document and design for non-functional and security requirements.
Use existing business conceptual data models to develop project-specific Solution Logical Data Models, defining required entities, attributes, relationships, and logical data structures.
Develop or assure Physical Data Models where required, in collaboration with Data Engineers and development teams.
Define patterns for exposing and consuming data through APIs, events, data pipelines, data products, and enterprise data-platform capabilities.
Define how AI and GenAI capabilities integrate with the overall solution, data, and technology architecture where relevant.
Provide architecture direction for solutions using strategic platforms such as Microsoft Azure and Databricks, applying appropriate cloud, lakehouse, API, integration, batch/streaming, and ETL/ELT patterns.
Apply client standards, reference architectures, and strategic technology roadmaps, identifying and managing required step-outs.
Work with business stakeholders, Data Architects, Data Engineers, application teams, security, platform teams, and vendors to resolve design issues and drive architecture decisions.
Produce architecture that is understandable to technical delivery teams and business stakeholders.
Provide architecture assurance throughout design and implementation to ensure the delivered solution conforms to the approved architecture and data models.