Senior Data Architect

Datavail•Boulder, CO

About The Position

Datavail is hiring a Senior Data Architect to lead the design and delivery of modern data platforms for clients, with a primary focus on Microsoft Fabric and the Azure data ecosystem. The architect owns client-engagement architecture from discovery through production, defines target-state architectures and data models, and establishes SDLC and DevOps practices for client teams. This is a client-facing role involving collaboration with business stakeholders, client IT and security teams, and Datavail data engineers, analytics engineers, and BI developers, often across multiple engagements. The role spans solution architecture, Microsoft Fabric and Azure platform design, data modeling, SDLC and DevOps, governance and security, AI and machine-learning enablement, consulting and delivery, and technical leadership. It includes leading discovery and assessments, developing architecture artifacts and recommendations, planning migrations, designing secure and governed platforms, establishing deployment and operational practices, and advising clients on data and AI architecture. The position also supports pre-sales, technical delivery, production readiness, account growth, reusable solution development, and evaluation of emerging technologies. The successful candidate will lead and mentor delivery teams, contribute to hiring and onboarding, present recommendations to technical and executive audiences, and travel to client sites up to 25%. A bachelor's degree in a related field or equivalent professional experience is required, along with extensive data and architecture experience, Azure and production Microsoft Fabric delivery experience, and authorization to work in the United States.

Requirements

Bachelor's degree in computer science, information systems, engineering, or a related field, or equivalent professional experience.
10 or more years of experience in data engineering, data warehousing, or analytics, including at least 5 years in a data or solution architect role.
At least 3 years designing and delivering data solutions on Azure, and hands-on production delivery experience with Microsoft Fabric.
End-to-end architecture ownership of at least two enterprise-scale data platform implementations.
Expert-level dimensional modeling skills and working knowledge of Data Vault 2.0 and normalized modeling.
Advanced T-SQL and Spark SQL skills; working proficiency in Python/PySpark; working knowledge of DAX and KQL.
Practical experience implementing CI/CD, Git-based workflows, and infrastructure as code for data platforms.
Experience in professional services, consulting, or client-facing delivery, including pre-sales and estimation.
Experience leading and mentoring technical teams.
Strong written and verbal communication skills, including producing clear architecture documentation and leading client workshops.
Ability to travel up to 25% to client sites.
Authorization to work in the United States.

Nice To Haves

Microsoft Certified: Fabric Data Engineer Associate (DP-700).
Microsoft Certified: Azure Solutions Architect Expert (AZ-305).
Microsoft Certified: DevOps Engineer Expert (AZ-400).
Databricks Certified Data Engineer Professional.
Production delivery of AI or machine-learning solutions, including RAG or generative AI workloads on Azure.
Experience designing ontologies or knowledge graphs and hands-on work with Fabric IQ or Foundry IQ.
Experience implementing AI governance or responsible AI programs, and GenAIOps evaluation and monitoring for production AI solutions.
Experience with Azure Government (GCC, GCC High) or other government cloud environments, including FedRAMP and related compliance requirements.
Experience architecting multi-tenant data platforms, including tenant isolation, onboarding automation, and per-tenant security and cost allocation.
Experience with transformation and data quality frameworks.
Industry experience in healthcare, financial services, manufacturing, retail, or the public sector.

Responsibilities

Lead discovery and current-state assessments of client data estates, including sources, integrations, platforms, data quality, security posture, and operating model.
Define target-state and transition architectures for analytics, data warehousing, Lakehouse, real-time, and operational data workloads.
Produce architecture documents, logical and physical diagrams, architecture decision records, non-functional requirements, and capacity plans.
Evaluate platform and build-versus-buy options across Fabric, Databricks, Azure Synapse Analytics, and Azure SQL; communicate recommendations, costs, risks, and effort.
Apply the Azure Well-Architected Framework and Cloud Adoption Framework cloud-scale analytics guidance to designs.
Design for scale and multi-domain ownership, including data mesh and hub-and-spoke patterns where appropriate.
Plan and lead migrations from on-premises SQL Server, SSIS, SSAS, legacy data warehouses, and Azure Synapse dedicated SQL pools to Fabric and Azure.
Lead architecture and design reviews; maintain reference architectures and standards; manage exceptions and track technical debt.
Architect end-to-end Microsoft Fabric solutions across OneLake, Lakehouse, Warehouse, Data Factory, Dataflows Gen2, Spark notebooks, Real-Time Intelligence, and Power BI.
Design OneLake strategy, including workspace and domain structure, shortcuts, mirroring, and medallion layering on Delta Lake.
Size and govern Fabric capacities; manage workload isolation, monitoring, throttling, and costs.
Forecast consumption, set budgets and tagging/showback standards, monitor unit economics, and optimize cloud and Fabric spending.
Design scalable Power BI semantic models, including storage modes, composite models, incremental refresh, and security.
Integrate Fabric with Azure services and other platforms, including ADLS Gen2, Azure Data Factory, Databricks, Azure SQL, Cosmos DB, Event Hubs, Azure Functions, and Azure Machine Learning.
Design network and identity controls using Microsoft Entra ID, managed identities, service principals, Key Vault, private endpoints, Private Link, managed virtual networks, and workspace access controls.
Own conceptual, logical, and physical data models, from business glossaries through deployed schemas.
Apply suitable data-modeling approaches, including Kimball dimensional modeling, Data Vault 2.0, and normalized 3NF models.
Design canonical and enterprise data models, master and reference data structures, and entity-resolution approaches.
Define naming and data-type standards, key strategies, and Delta table partitioning and file layout.
Maintain models in modeling tools and version control alongside code.
Establish data workload delivery lifecycles, including environment strategy, branching, pull requests, code review, releases, and rollback.
Implement Fabric Git integration, Fabric deployment pipelines, and automated deployment through Fabric REST APIs and fabric-cicd.
Build CI/CD pipelines for Fabric, Azure Data Factory, Databricks, SQL database projects, and Power BI.
Define infrastructure as code using Bicep and Terraform for Azure resources, identities, and networking.
Embed automated unit, data quality, contract, schema-drift, and semantic-model regression tests.
Define DataOps observability, alerting, lineage, pipeline SLAs, incident response, and runbooks.
Define support models, SLAs/SLOs, escalation paths, training, documentation, and handoff criteria.
Work within Agile delivery using Azure Boards or Jira, including backlog refinement, estimation, and sprint planning.
Set standards and quality guardrails for AI-assisted development with GitHub Copilot and AI coding agents.
Design governance frameworks aligned to DAMA-DMBOK for ownership, stewardship, metadata, data quality, lifecycle, and retention.
Implement Microsoft Purview for catalog, lineage, classification, and sensitivity labels across Fabric and Azure sources.
Design least-privilege security models for Fabric workspaces, items, OneLake, and row-, column-, and object-level access.
Ensure solutions address client regulatory obligations, working with compliance and security teams.
Establish responsible AI and AI governance aligned to relevant frameworks and standards, including model-use approvals, data-access policies, and human oversight.
Design AI workload security controls addressing prompt injection, data exfiltration, content filtering, and Purview data security.
Design governed data foundations for AI and machine learning, including curated feature data, data quality, and access for training and inference.
Architect solutions using Azure Machine Learning, Fabric Data Science, Azure OpenAI, and RAG over enterprise data with Azure AI Search.
Prepare client data estates and semantic models for Copilot in Fabric and Power BI.
Design Fabric IQ solutions, ontologies, and Fabric data agents for conversational Q&A over ontology-grounded data.
Model knowledge graphs for multi-hop reasoning using Graph in Fabric and other graph technologies where appropriate.
Architect agentic solutions with Microsoft Foundry and Foundry IQ, grounding agents in structured and unstructured sources.
Define platform placement for AI agents and models and how agents access governed data through APIs, tools, and MCP servers.
Design GenAIOps practices for response evaluation, groundedness, tracing, observability, prompt and model versioning, and token and cost monitoring.
Plan AI costs and capacity, including model selection, deployment approaches, and impacts on Fabric and Databricks compute.
Support pre-sales by leading technical discovery, shaping solutions, estimating effort, and contributing to proposals and statements of work.
Serve as the technical lead on client engagements, owning technical approach and architecture standards, aligning delivery to scope and budget, acting as the primary technical contact, and raising risks and change requests.
Review implementation against approved designs, conduct readiness reviews, and provide production sign-off.
Present architecture and recommendations in writing and in person to audiences ranging from engineers to C-level executives.
Identify follow-on opportunities and contribute to account growth based on client needs.
Develop solution accelerators and Datavail intellectual property, including reference architectures, reusable frameworks, deployment templates, and packaged offerings.
Evaluate new and preview capabilities across Microsoft Fabric, Azure, data, and AI; build proofs of concept and turn successful findings into practice guidance and client recommendations.
Convert recurring patterns and lessons into reusable intellectual property while protecting client confidentiality.
Lead technical delivery teams of data engineers, analytics engineers, and BI developers.
Mentor team members through design and code reviews, pairing, and certification guidance.
Contribute to hiring, interviewing, and onboarding for the Data & Analytics practice.