{"id":3532729,"url":"https://alion.io/job/datavail-senior-data-architect","title":"Senior Data Architect","company":{"id":2064706,"name":"Datavail","domain":"datavail.com","url":"https://alion.io/company/datavail","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Boulder, United 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2","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"SSAS","optional":false},{"name":"SSIS","optional":false},{"name":"Terraform","optional":false},{"name":"FedRAMP","optional":true},{"name":"PowerShell","optional":true}],"status":"live","first_seen_at":"2026-10-30T16:19:19Z","employer_posted_date":"2026-10-30","last_verified_at":"2026-10-31T02:42:06Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"ABOUT THE ROLE\nDatavail is hiring a Senior Data Architect to lead the design and delivery of modern data platforms for our clients\nYou will own the architecture of client engagements from discovery through production, with a primary focus on Microsoft Fabric and the Azure data ecosystem\nYou will define target-state architectures, build the data models that underpin them, and establish the SDLC and DevOps practices that let client teams operate and extend what we deliver\nThis is a client-facing role\nYou will work directly with business stakeholders, client IT and security teams, and Datavail delivery teams of data engineers, analytics engineers and BI developers, often across several engagements at once\nRESPONSIBILITIES\nArchitecture and solution design\nLead discovery and current-state assessments of client data estates, including sources, integrations, platforms, data quality, security posture and operating model\nDefine target-state and transition architectures for analytics, data warehousing, Lakehouse, real-time and operational data workloads\nProduce architecture deliverables: solution architecture documents, logical and physical architecture diagrams, architecture decision records (ADRs), non-functional requirements and capacity plans\nEvaluate platform and build-versus-buy trade-offs across Fabric, Databricks, Azure Synapse Analytics and Azure SQL, and present recommendations with cost, risk and effort clearly stated\nApply the Azure Well-Architected Framework (reliability, security, cost optimization, operational excellence, performance efficiency) and the Cloud Adoption Framework's cloud-scale analytics guidance to every design\nDesign for scale and multi-domain ownership, including data mesh and hub-and-spoke patterns where they fit the client's organization\nPlan and lead migrations from on-premises SQL Server, SSIS, SSAS and legacy data warehouses, and from Azure Synapse dedicated SQL pools, to Fabric and Azure\nLead architecture and design reviews, maintain reference architectures and standards, manage exceptions, and track technical debtMicrosoft Fabric and Azure platform\n\nArchitect end-to-end Fabric solutions across OneLake, Lakehouse, Warehouse, Data Factory pipelines, Dataflows Gen2, Spark notebooks, Real-Time Intelligence (Eventstream, Eventhouse, KQL) and Power BI\nDesign OneLake strategy: workspace and domain structure, shortcuts to ADLS Gen2 and external storage, database mirroring, and medallion (bronze/silver/gold) layering on Delta Lake\nSize and govern Fabric capacities (F SKUs), including workload isolation, capacity monitoring, throttling behavior and cost management\nForecast consumption, set budgets and tagging/showback standards, monitor unit economics, and optimize cloud/Fabric spend\nDesign Power BI semantic models for performance and scale, including Direct Lake, Import and DirectQuery storage modes, composite models, incremental refresh, and row-level and object-level security\nIntegrate Fabric with the wider Azure estate: ADLS Gen2, Azure Data Factory, Databricks and Unity Catalog, Azure SQL Database and Managed Instance, Cosmos DB, Event Hubs, Azure Functions and Azure Machine Learning\nDesign network and identity controls: Microsoft Entra ID, managed identities and service principals, Azure Key Vault, private endpoints and Private Link, managed virtual networks and workspace-level access\nData modeling\nOwn conceptual, logical and physical data models for client solutions, from business glossary through to deployed schemas\nApply the right modeling approach for each layer: Kimball dimensional modeling (star schemas, conformed dimensions, bus matrix, slowly changing dimensions Types 1, 2 and 6), Data Vault 2.0 (hubs, links, satellites) for integration layers, and normalized 3NF models for operational and master data\nDesign canonical and enterprise data models, master and reference data structures, and entity resolution approaches\nDefine naming standards, data type conventions, key strategies (surrogate, natural, hash), and partitioning and file layout for Delta tables\nMaintain models in a modeling tool (erwin Data Modeler, ER/Studio, SqlDBM or equivalent) and keep them in version control alongside code\nSDLC and DevOps\nEstablish the delivery lifecycle for data workloads: environment strategy (dev, test, prod), branching and pull request standards, code review, release management and rollback\nImplement Fabric Git integration with Azure DevOps or GitHub, Fabric deployment pipelines, and automated deployment using the Fabric REST APIs and the fabric-cicd library\nBuild CI/CD pipelines in Azure DevOps Pipelines and GitHub Actions for Fabric items, Azure Data Factory, Databricks (Asset Bundles), SQL database projects (DACPAC/sqlpackage) and Power BI (PBIP/TMDL)\nDefine infrastructure as code with Bicep and Terraform for Azure resources, identities and networking\nEmbed automated testing: unit tests for transformations, data quality and contract tests, schema-drift detection, and regression testing of semantic models\nDefine DataOps practices for observability, alerting, lineage, pipeline SLAs and incident response, and hand them over with runbooks\nDefine support models, SLAs/SLOs, escalation paths, training, documentation, and handoff criteria\nWork within Agile delivery (Scrum or Kanban) using Azure Boards or Jira, including backlog refinement, estimation and sprint planning\nSet standards for AI-assisted development with GitHub Copilot and AI coding agents across design, code, testing and documentation, including review and quality guardrails\nData governance and security\nDesign governance frameworks aligned to DAMA-DMBOK, covering data ownership and stewardship, metadata, data quality, lifecycle and retention\nImplement Microsoft Purview for data catalog, lineage, classification and sensitivity labels across Fabric and Azure sources\nDesign least-privilege security models using Fabric workspace roles, item permissions, OneLake security, and row-, column- and object-level security\nEnsure solutions meet client regulatory obligations such as HIPAA, SOC 2, PCI DSS, GDPR and CCPA, working with client compliance and security teams\nEstablish responsible AI and AI governance aligned to the NIST AI Risk Management Framework, the EU AI Act and Microsoft's Responsible AI Standard, including approved model use, data access policies for AI, and human oversight\nDesign security controls for AI workloads, covering prompt injection, data exfiltration through agents, content filtering with Azure AI Content Safety, and Microsoft Purview data security for AI\nAI and machine learning enablement\nDesign data foundations that support AI and machine learning: curated feature data, data quality controls and governed access for model training and inference\nArchitect solutions that use Azure Machine Learning, Fabric Data Science and Azure OpenAI, including retrieval-augmented generation (RAG) over enterprise data with Azure AI Search\nPrepare client data estates and semantic models for Copilot in Fabric and Power BI\nDesign Fabric IQ solutions, including ontologies that define business entities, relationships, rules and data bindings to lakehouses, eventhouses and Power BI semantic models, and Fabric data agents for conversational Q&A over ontology-grounded dataModel knowledge graphs for multi-hop reasoning over connected enterprise data, using Graph in Fabric and other graph technologies where appropriate\n\nArchitect agentic solutions with Microsoft Foundry and Foundry IQ, using knowledge sources and knowledge bases that ground agents in both unstructured content and structured Fabric data\nDefine where AI agents and models sit in the overall platform, including when to use Fabric, Foundry or Databricks, and how agents access governed data through APIs, tools and Model Context Protocol (MCP) servers\nDesign GenAIOps practices: evaluation of response quality and groundedness, tracing and observability, prompt and model versioning, and monitoring of token usage and cost\nPlan AI cost and capacity, including model selection, provisioned versus pay-as-you-go deployments, and the impact of AI workloads on Fabric and Databricks compute\nConsulting, pre-sales and delivery\nSupport pre-sales: lead technical discovery calls, shape solution approaches, build effort estimates and contribute to proposals and statements of work (SOWs), including scope, assumptions, deliverables and acceptance criteria\nFulfill the technical lead role on client engagements: own the technical approach and architecture standards, keep delivery aligned to scope and budget, serve as the primary technical contact for client teams, and raise risks and change requests early\nReview implementation against approved designs, conduct readiness reviews, and provide production sign-off\nPresent architecture and recommendations to audiences from engineers to C-level executives, in writing and in person\nIdentify follow-on opportunities and contribute to account growth based on client needs\nDevelop solution accelerators and Datavail intellectual property (IP), such as reference architectures, reusable frameworks, deployment templates and packaged offerings, that speed up delivery and differentiate the practice\nStay ahead of innovation across Microsoft Fabric, Azure and the wider data and AI market: evaluate preview and newly released capabilities, build proofs of concept, and turn what works into practice guidance and client recommendations\nConvert recurring patterns, decisions, and lessons learned into reusable Datavail intellectual property-including architecture blueprints, decision frameworks, assessment tools, checklists, templates, and accelerators-while protecting client confidentiality\nLeadership and mentoring\nLead technical delivery teams of data engineers, analytics engineers and BI developers on client engagements\nMentor team members through design reviews, code reviews, pairing and certification guidanceContribute to hiring, interviewing and onboarding for the Data & Analytics practice\n\nREQUIRED QUALIFICATIONS\nBachelor's degree in computer science, information systems, engineering or a related field, or equivalent professional experience\n10+ years of experience in data engineering, data warehousing or analytics, including 5+ years in a data or solution architect role\n3+ years designing and delivering data solutions on Azure, and hands-on delivery experience with Microsoft Fabric in production\nEnd-to-end architecture ownership of at least two enterprise-scale data platform implementations\nExpert-level dimensional modeling skills and working knowledge of Data Vault 2.0 and normalized modeling\nAdvanced SQL (T-SQL and Spark SQL) and working proficiency in Python/PySpark. Working knowledge of DAX and KQL\nPractical experience implementing CI/CD, Git-based workflows and infrastructure as code for data platforms\nExperience in a professional services, consulting or client-facing delivery environment, including pre-sales and estimation\nExperience leading and mentoring technical teams\nStrong written and verbal communication, with the ability to produce clear architecture documentation and lead client workshops\nAbility to travel up to 25% to client sites\nAuthorization to work in the United States \nPREFERRED QUALIFICATIONS\nMicrosoft Certified: Fabric Data Engineer Associate (DP-700)\nMicrosoft Certified: Azure Solutions Architect Expert (AZ-305)\nMicrosoft Certified: DevOps Engineer Expert (AZ-400)\nDatabricks Certified Data Engineer Professional\nDelivered AI or machine learning solutions in production, including RAG or generative AI workloads on Azure\nExperience designing ontologies or knowledge graphs, and hands-on work with Fabric IQ or Foundry IQ\nExperience implementing AI governance or responsible AI programs, and GenAIOps evaluation and monitoring for production AI solutions\nExperience with Azure Government (GCC, GCC High) or other government cloud environments, including FedRAMP and related compliance requirements\nExperience architecting multi-t...","description_format":"text","description_chars":13571,"description_truncated":true,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Information Technology","Databases & DBMS","Cloud Consulting & Migration"],"lifecycle":[{"event":"open","at":"2026-10-30T16:35:56Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 15 · green card 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