Senior Data Architect

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.