Senior Data Architect

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.…