Archived listing. The details below describe a past opening.
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Architect based in the United States.
We are seeking an experienced Senior Data Architect to lead the design and delivery of modern, enterprise-scale data platforms, with a strong focus on Microsoft Fabric and the Azure ecosystem. In this client-facing role, you will own architecture engagements from initial discovery and solution design through implementation and production delivery. You will define scalable data architectures, develop robust data models, and establish engineering standards, governance frameworks, and DevOps practices. Working directly with business stakeholders, technical teams, and executive leaders, you will translate complex requirements into practical solutions that deliver measurable value. You will also shape data and AI strategies, supporting advanced analytics, machine learning, and generative AI initiatives. This is an opportunity to combine deep technical expertise, consulting leadership, and team mentorship while helping organizations modernize their data capabilities.
Accountabilities
As a Senior Data Architect, you will provide end-to-end technical leadership across multiple client engagements, ensuring that data platforms are scalable, secure, cost-effective, and aligned with business objectives. You will guide architecture decisions, oversee implementation standards, and help clients maximize the value of Microsoft Fabric, Azure, and emerging AI technologies.
• Architecture and solution design
• Lead discovery sessions and assessments of client data environments, evaluating data sources, integrations, platforms, quality, security, and operating models.
• Define target-state and transition architectures for analytics, data warehousing, Lakehouse, real-time, and operational data workloads.
• Produce architecture documentation, including solution designs, logical and physical diagrams, architecture decision records, non-functional requirements, and capacity plans.
• Evaluate platform options and build-versus-buy decisions across Microsoft Fabric, Databricks, Azure Synapse Analytics, and Azure SQL, clearly communicating costs, risks, and implementation effort.
• Apply the Azure Well-Architected Framework and Cloud Adoption Framework to ensure reliability, security, cost optimization, operational excellence, and performance efficiency.
• Design scalable architectures supporting multiple data domains, including data mesh and hub-and-spoke approaches where appropriate.
• Lead migrations from legacy platforms, including on-premises SQL Server, SSIS, SSAS, traditional data warehouses, and Azure Synapse dedicated SQL pools.
• Lead architecture reviews, maintain reference architectures and technical standards, manage exceptions, and track technical debt.
• Microsoft Fabric and Azure platform architecture
• Design end-to-end Microsoft Fabric solutions using OneLake, Lakehouse, Warehouse, Data Factory pipelines, Dataflows Gen2, Spark notebooks, Real-Time Intelligence, and Power BI.
• Define OneLake strategies, including workspace and domain structures, external storage shortcuts, database mirroring, and medallion architecture using Delta Lake.
• Size and govern Fabric capacities, including workload isolation, monitoring, throttling, and cost management.
• Forecast platform consumption, establish budgets and cost-allocation standards, and optimize cloud and Fabric spending.
• Design high-performance Power BI semantic models using Direct Lake, Import, DirectQuery, composite models, incremental refresh, and row-level and object-level security.
• Integrate Fabric with Azure services and platforms such as ADLS Gen2, Azure Data Factory, Databricks, Unity Catalog, Azure SQL, Cosmos DB, Event Hubs, Azure Functions, and Azure Machine Learning.
• Define identity and network security controls using Microsoft Entra ID, managed identities, service principals, Azure Key Vault, private endpoints, Private Link, and managed virtual networks.
• Data modeling
• Own conceptual, logical, and physical data models, from business glossaries through to deployed database schemas.
• Apply appropriate modeling methodologies, including Kimball dimensional modeling, Data Vault 2.0, and normalized third normal form (3NF) models.
• Design enterprise data models, canonical structures, master and reference data frameworks, and entity resolution approaches.
• Establish naming conventions, data types, key strategies, partitioning, and file layout standards for Delta tables.
• Maintain data models using tools such as erwin Data Modeler, ER/Studio, SqlDBM, or equivalent, with models version-controlled alongside implementation code.
• SDLC, DevOps, and DataOps
• Establish development lifecycles and environment strategies covering development, testing, and production.
• Define branching, pull request, code review, release management, and rollback standards.
• Implement Fabric Git integration with Azure DevOps or GitHub, deployment pipelines, and automated deployments using Fabric REST APIs and the fabric-cicd library.
• Build CI/CD pipelines for Fabric, Azure Data Factory, Databricks, SQL database projects, and Power BI using Azure DevOps Pipelines and GitHub Actions.
• Define infrastructure-as-code practices using Bicep and Terraform for Azure resources, identities, and networking.
• Implement automated testing, including unit tests, data quality and contract tests, schema-drift detection, and semantic model regression testing.
• Establish DataOps standards for observability, alerting, data lineage, pipeline SLAs, and incident response.
• Define operational support models, SLAs and SLOs, escalation procedures, runbooks, documentation, and knowledge-transfer requirements.
• Participate in Agile delivery practices, including backlog refinement, estimation, and sprint planning through Azure Boards or Jira.
• Establish standards and quality controls for AI-assisted development using GitHub Copilot and AI coding agents across architecture, coding, testing, and documentation.
• Data governance and security
• Design governance frameworks aligned with DAMA-DMBOK, covering ownership, stewardship, metadata, data quality, lifecycle, and retention.
• Implement Microsoft Purview for data cataloging, lineage, classification, and sensitivity labeling across Fabric and Azure environments.
• Define least-privilege access models using Fabric workspace roles, item permissions, OneLake security, and row-, column-, and object-level controls.
• Ensure solutions meet applicable regulatory requirements, including HIPAA, SOC 2, PCI DSS, GDPR, and CCPA.
• Establish responsible AI and AI governance practices aligned with the NIST AI Risk Management Framework, EU AI Act, and Microsoft's Responsible AI Standard.
• Design security controls for AI workloads, addressing prompt injection, data exfiltration through agents, content filtering, and Microsoft Purview data security.
• AI and machine learning enablement
• Design governed data foundations for AI and machine learning, including curated feature data, quality controls, and secure access for training and inference.
• Architect solutions using Azure Machine Learning, Fabric Data Science, and Azure OpenAI.
• Design retrieval-augmented generation (RAG) solutions over enterprise data using Azure AI Search.
• Prepare client data environments and semantic models for Copilot in Fabric and Power BI.
• Design Fabric IQ solutions, including ontologies, business entities, relationships, rules, and data bindings to Lakehouse, Eventhouse, and Power BI semantic models.
• Architect Fabric data agents for conversational queries grounded in governed enterprise data.
• Develop knowledge graph solutions supporting multi-hop reasoning through Graph in Fabric and other appropriate graph technologies.
• Design agentic solutions using Microsoft Foundry and Foundry IQ, connecting agents to structured and unstructured enterprise knowledge.
• Define how AI agents and models integrate into the wider platform, including the use of Fabric, Foundry, and Databricks, as well as APIs, tools, and Model Context Protocol (MCP) servers.
• Establish GenAIOps practices covering response quality, groundedness, tracing, observability, prompt and model versioning, and token usage and cost monitoring.
• Plan AI capacity and spending, evaluating model selection, deployment options, and the impact of AI workloads on Fabric and Databricks resources.
• Consulting, pre-sales, and delivery leadership
• Lead technical discovery during pre-sales engagements, shape solution approaches, estimate effort, and contribute to proposals and statements of work.
• Define project scope, assumptions, deliverables, and acceptance criteria.
• Serve as the technical lead for client engagements, owning architecture decisions, delivery standards, scope alignment, and technical risk management.
• Act as the primary technical contact for client teams, communicating risks and change requests proactively.
• Review implementation work against approved architecture, conduct readiness assessments, and provide production sign-off.
• Present technical recommendations to audiences ranging from engineering teams to C-level executives.
• Identify opportunities to expand client relationships through additional solutions and services.
• Develop reusable architecture blueprints, frameworks, deployment templates, assessment tools, and other solution accelerators.
• Evaluate emerging Microsoft Fabric, Azure, data, and AI capabilities, develop proofs of concept, and translate successful innovations into client recommendations.
• Convert recurring technical patterns and lessons learned into reusable intellectual property while protecting client confidentiality.
• Leadership and mentoring
• Lead technical delivery teams comprising data engineers, analytics engineers, and BI developers.
• Mentor team members through architecture and code reviews, collaborative problem-solving, pairing, and certification guidance.
• Contribute to recruitment, technical interviews, and onboarding within the Data and Analytics practice.
Requirements
The ideal candidate will combine extensive data architecture experience with hands-on expertise in Microsoft Fabric and Azure. You should have a strong background in enterprise data modeling, modern engineering practices, cloud architecture, and client-facing consulting, along with the leadership skills needed to guide technical teams and influence senior stakeholders.
• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent professional experience.
• At least 10 years of experience in data engineering, data warehousing, or analytics, including 5+ years in a data or solution architecture role.
• At least 3 years of experience designing and delivering Azure data solutions, including hands-on production experience with Microsoft Fabric.
• Proven end-to-end architecture ownership of at least two enterprise-scale data platform implementations.
• Expert-level knowledge of dimensional modeling and working experience with Data Vault 2.0 and normalized data modeling.
• Advanced SQL skills, including T-SQL and Spark SQL, and working proficiency in Python and PySpark.
• Working knowledge of DAX and KQL.
• Practical experience implementing CI/CD, Git-based workflows, and infrastructure-as-code practices for data platforms.
• Experience in professional services, consulting, or client-facing delivery environments, including pre-sales and effort estimation.
• Demonstrated experience leading and mentoring technical teams.
• Strong written and verbal communication skills, including 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.
• Experience delivering production AI or machine learning solutions, including RAG and generative AI workloads on Azure.
• Experience designing ontologies or knowledge graphs, with hands-on exposure to Fabric IQ or Foundry IQ.
• Experience implementing AI governance, responsible AI programs, and GenAIOps evaluation and monitoring.
• Experience with Azure Government environments, including GCC, GCC High, FedRAMP, and related compliance requirements.
• Experience architecting multi-tenant data platforms, including tenant isolation, automated onboarding, and tenant-level security and cost allocation.
• Experience with data transformation and data quality frameworks.
• Industry experience in healthcare, financial services, manufacturing, retail, or the public sector.
Technical Environment
The role involves working across a broad range of modern data, cloud, AI, and engineering technologies, including:
• Microsoft Fabric: OneLake, Lakehouse, Warehouse, Data Factory, Dataflows Gen2, Spark notebooks, Real-Time Intelligence, database mirroring, shortcuts, Power BI, Direct Lake, and deployment pipelines.
• Azure data services: ADLS Gen2, Azure Data Factory, Databricks, Unity Catalog, Azure Synapse Analytics, Azure SQL, Cosmos DB, Event Hubs, and Stream Analytics.
• AI and machine learning: Azure Machine Learning, Fabric Data Science, Azure OpenAI, Azure AI Search, Copilot in Fabric, Microsoft Foundry, Foundry IQ, Azure AI Content Safety, and MCP.
• Semantics and knowledge: Fabric IQ, ontologies, Graph in Fabric, Fabric data agents, knowledge graphs, and Power BI semantic models.
• Security and governance: Microsoft Entra ID, Azure Key Vault, Private Link, managed virtual networks, and Microsoft Purview.
• DevOps and automation: Azure DevOps, GitHub, GitHub Actions, Fabric Git integration, fabric-cicd, Databricks Asset Bundles, SQL database projects, Bicep, Terraform, and GitHub Copilot.
• Programming languages: T-SQL, Spark SQL, Python, PySpark, DAX, KQL, and PowerShell.
• Data modeling: Kimball dimensional modeling, Data Vault 2.0, 3NF, medallion architecture, erwin, ER/Studio, and SqlDBM.
• Architecture and delivery frameworks: Azure Well-Architected Framework, Cloud Adoption Framework, DAMA-DMBOK, data mesh, Scrum, and Kanban.
Benefits
The position offers the opportunity to:
• Lead high-impact enterprise data architecture projects across a diverse range of industries.
• Work with modern Microsoft Fabric, Azure, and AI technologies.
• Influence technical strategy and architecture standards across multiple client engagements.
• Develop reusable solutions, frameworks, and intellectual property.
• Mentor and develop technical professionals while contributing to the growth of a data and analytics practice.
• Explore emerging technologies and turn innovation into practical client solutions.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
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