ABOUT THE ROLE
- Datavail 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
- Architecture and solution design
- 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 deliverables: solution architecture documents, logical and physical architecture diagrams, architecture decision records (ADRs), non-functional requirements and capacity plans
- Evaluate 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
- Apply 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
- Design for scale and multi-domain ownership, including data mesh and hub-and-spoke patterns where they fit the client's organization
- Plan 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
- Lead architecture and design reviews, maintain reference architectures and standards, manage exceptions, and track technical debt
Microsoft Fabric and Azure platform - Architect 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
- Design OneLake strategy: workspace and domain structure, shortcuts to ADLS Gen2 and external storage, database mirroring, and medallion (bronze/silver/gold) layering on Delta Lake
- Size and govern Fabric capacities (F SKUs), including workload isolation, capacity monitoring, throttling behavior and cost management
- Forecast consumption, set budgets and tagging/showback standards, monitor unit economics, and optimize cloud/Fabric spend
- Design 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
- Integrate 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
- Design 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
- Data modeling
- Own conceptual, logical and physical data models for client solutions, from business glossary through to deployed schemas
- Apply 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
- Design canonical and enterprise data models, master and reference data structures, and entity resolution approaches
- Define naming standards, data type conventions, key strategies (surrogate, natural, hash), and partitioning and file layout for Delta tables
- Maintain models in a modeling tool (erwin Data Modeler, ER/Studio, SqlDBM or equivalent) and keep them in version control alongside code
- SDLC and DevOps
- Establish the delivery lifecycle for data workloads: environment strategy (dev, test, prod), branching and pull request standards, code review, release management and rollback
- Implement Fabric Git integration with Azure DevOps or GitHub, Fabric deployment pipelines, and automated deployment using the Fabric REST APIs and the fabric-cicd library
- Build 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 networking
- Embed automated testing: unit tests for transformations, data quality and contract tests, schema-drift detection, and regression testing of semantic models
- Define DataOps practices for observability, alerting, lineage, pipeline SLAs and incident response, and hand them over with runbooks
- Define support models, SLAs/SLOs, escalation paths, training, documentation, and handoff criteria
- Work within Agile delivery (Scrum or Kanban) using Azure Boards or Jira, including backlog refinement, estimation and sprint planning
- Set standards for AI-assisted development with GitHub Copilot and AI coding agents across design, code, testing and documentation, including review and quality guardrails
- Data governance and security
- Design governance frameworks aligned to DAMA-DMBOK, covering data ownership and stewardship, metadata, data quality, lifecycle and retention
- Implement Microsoft Purview for data catalog, lineage, classification and sensitivity labels across Fabric and Azure sources
- Design least-privilege security models using Fabric workspace roles, item permissions, OneLake security, and row-, column- and object-level security
- Ensure solutions meet client regulatory obligations such as HIPAA, SOC 2, PCI DSS, GDPR and CCPA, working with client compliance and security teams
- Establish 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
- Design 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
- AI and machine learning enablement
- Design data foundations that support AI and machine learning: curated feature data, data quality controls and governed access for model training and inference
- Architect solutions that use Azure Machine Learning, Fabric Data Science and Azure OpenAI, including retrieval-augmented generation (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, 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 data
Model knowledge graphs for multi-hop reasoning over connected enterprise data, using Graph in Fabric and other graph technologies where appropriate - Architect 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
- Define 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
- Design GenAIOps practices: evaluation of response quality and groundedness, tracing and observability, prompt and model versioning, and monitoring of token usage and cost
- Plan 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
- Consulting, pre-sales and delivery
- Support 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
- Fulfill 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
- Review implementation against approved designs, conduct readiness reviews, and provide production sign-off
- Present architecture and recommendations to audiences from engineers to C-level executives, in writing and in person
- Identify follow-on opportunities and contribute to account growth based on client needs
- Develop 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
- Stay 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
- Convert 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
- Leadership and mentoring
- Lead technical delivery teams of data engineers, analytics engineers and BI developers on client engagements
- Mentor team members through design reviews, code reviews, pairing and certification guidance
Contribute to hiring, interviewing and onboarding for the Data & Analytics practice
REQUIRED QUALIFICATIONS
- Bachelor's degree in computer science, information systems, engineering or a related field, or equivalent professional experience
- 10+ years of experience in data engineering, data warehousing or analytics, including 5+ years in a data or solution architect role
- 3+ years designing and delivering data solutions on Azure, and hands-on delivery experience with Microsoft Fabric in production
- 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 SQL (T-SQL and Spark SQL) and 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 a professional services, consulting or client-facing delivery environment, including pre-sales and estimation
- Experience leading and mentoring technical teams
- Strong written and verbal communication, with the ability to produce clear architecture documentation and lead client workshops
- Ability to travel up to 25% to client sites
- Authorization to work in the United States
PREFERRED QUALIFICATIONS
- 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
- Delivered AI or machine learning solutions in production, 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 models (workspace-, capacity-, schema- or row-level), tenant 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
TECHNICAL ENVIRONMENT
- Area Technologies and frameworks
Microsoft Fabric
- OneLake, Lakehouse
- Warehouse, Data Factory
- Dataflows Gen2, Spark notebooks
- Real-Time Intelligence
- Mirroring
- Shortcuts
- Power BI, Direct Lake
- Deployment pipelines
Azure data
- ADLS Gen2
- Azure Data Factory
- Databricks, Unity Catalog
- Azure Synapse Analytics
- Azure SQL
- Cosmos DB
- Event Hubs
- Stream Analytics
AI and ML
- Azure Machine Learning
- Fabric Data Science
- Azure OpenAI
- Azure AI Search
- Copilot in Fabric
- Microsoft Foundry
- Foundry IQ, Azure AI Content Safety, Model Context Protocol (MCP)
Semantics and knowledge
- Fabric IQ, ontology
- Graph in Fabric
- Fabric data agents
- Knowledge graphs
- Power BI semantic models
Security and governance
- Microsoft Entra ID
- Azure Key Vault
- Private Link
- Managed VNets
- Microsoft Purview
DevOps
- Azure DevOps (Repos, Pipelines, Boards)
- GitHub and GitHub Actions
- Fabric Git integration
- Fabric-cicd
- Databricks Asset Bundles
- SQL database projects
- Bicep, Terraform
- GitHub Copilot
Languages
- T-SQL
- Spark SQL
- Python
- PySpark
- DAX, KQL
- PowerShell
Modeling
- Kimball dimensional
- Data Vault 2.0
- 3NF
- Medallion architecture; erwin
- ER/Studio
- SqlDBM
Frameworks
- Azure Well-Architected Framework
- Cloud Adoption Framework
- DAMA-DMBOK
- Data mesh
- Agile (Scrum, Kanban)
Originally posted on