Senior Data Architect

Senior Data ArchitectDatavail is hiring a Senior Data Architect to lead the design and delivery of modern data platforms for our clients. You will own the architecture of client engagements from discovery through production, with a primary focus on Microsoft Fabric and the Azure data ecosystem. You 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. This is a client-facing role. You 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.Responsibilities include:Architecture and solution designLead discovery and current-state assessments of client data estates, including sources, integrations, platforms, data quality, security posture and operating modelDefine target-state and transition architectures for analytics, data warehousing, Lakehouse, real-time and operational data workloadsProduce architecture deliverables: solution architecture documents, logical and physical architecture diagrams, architecture decision records (ADRs), non-functional requirements and capacity plansEvaluate 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 statedApply 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 designDesign for scale and multi-domain ownership, including data mesh and hub-and-spoke patterns where they fit the client's organizationPlan and lead migrations from on-premises SQL Server, SSIS, SSAS and legacy data warehouses, and from Azure Synapse dedicated SQL pools, to Fabric and AzureLead architecture and design reviews, maintain reference architectures and standards, manage exceptions, and track technical debtArchitect end-to-end Fabric solutions across OneLake, Lakehouse, Warehouse, Data Factory pipelines, Dataflows Gen2, Spark notebooks, Real-Time Intelligence (Eventstream, Eventhouse, KQL) and Power BIDesign OneLake strategy: workspace and domain structure, shortcuts to ADLS Gen2 and external storage, database mirroring, and medallion (bronze/silver/gold) layering on Delta LakeSize and govern Fabric capacities (F SKUs), including workload isolation, capacity monitoring, throttling behavior and cost managementForecast consumption, set budgets and tagging/showback standards, monitor unit economics, and optimize cloud/Fabric spendDesign 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 securityIntegrate 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 LearningDesign 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 accessData modelingOwn conceptual, logical and physical data models for client solutions, from business glossary through to deployed schemasApply 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 dataDesign canonical and enterprise data models, master and reference data structures, and entity resolution approachesDefine naming standards, data type conventions, key strategies (surrogate, natural, hash), and partitioning and file layout for Delta tablesMaintain models in a modeling tool (erwin Data Modeler, ER/Studio, SqlDBM or equivalent) and keep them in version control alongside codeSDLC and DevOpsEstablish the delivery lifecycle for data workloads: environment strategy (dev, test, prod), branching and pull request standards, code review, release management and rollbackImplement Fabric Git integration with Azure DevOps or GitHub, Fabric deployment pipelines, and automated deployment using the Fabric REST APIs and the fabric-cicd libraryBuild 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)Define infrastructure as code with Bicep and Terraform for Azure resources, identities and networkingEmbed automated testing: unit tests for transformations, data quality and contract tests, schema-drift detection, and regression testing of semantic modelsDefine DataOps practices for observability, alerting, lineage, pipeline SLAs and incident response, and hand them over with runbooksDefine support models, SLAs/SLOs, escalation paths, training, documentation, and handoff criteriaWork within Agile delivery (Scrum or Kanban) using Azure Boards or Jira, including backlog refinement, estimation and sprint planningSet standards for AI-assisted development with GitHub Copilot and AI coding agents across design, code, testing and documentation, including review and quality guardrailsData governance and securityDesign governance frameworks aligned to DAMA-DMBOK, covering data ownership and stewardship, metadata, data quality, lifecycle and retentionImplement Microsoft Purview for data catalog, lineage, classification and sensitivity labels across Fabric and Azure sourcesDesign least-privilege security models using Fabric workspace roles, item permissions, OneLake security, and row-, column- and object-level securityEnsure solutions meet client regulatory obligations such as HIPAA, SOC 2, PCI DSS, GDPR and CCPA, working with client compliance and security teamsEstablish 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 oversightDesign 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 AIAI and machine learning enablementDesign data foundations that support AI and machine learning: curated feature data, data quality controls and governed access for model training and inferenceArchitect solutions that use Azure Machine Learning, Fabric Data Science and Azure OpenAI, including retrieval-augmented generation (RAG) over enterprise data with Azure AI SearchPrepare client data estates and semantic models for Copilot in Fabric and Power BIDesign 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 appropriateArchitect agentic solutions with Microsoft Foundry and Foundry IQ, using knowledge sources and knowledge bases that ground agents in both unstructured content and structured Fabric dataDefine 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) serversDesign GenAIOps practices: evaluation of response quality and groundedness, tracing and observability, prompt and model versioning, and monitoring of token usage and costPlan AI cost and capacity, including model selection, provisioned versus pay-as-you-go deployments, and the impact of AI workloads on Fabric and Databricks computeConsulting, pre-sales and deliverySupport 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 criteriaFulfill 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 earlyReview implementation against approved designs, conduct readiness reviews, and provide production sign-off