Architecture / architect

Microsoft Fabric - L55 - Fabric Architect/Engineer
Join our AI & Engineering team in transforming technology platforms, driving innovation, and helping make a significant impact on our clients' success. You’ll work alongside talented professionals reimagining and re-engineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.
AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. As a Lead Data Engineer II on the AI & Data team, you will be responsible for leading the design and delivery of modern data and analytics solutions for client engagements. In this role, you will bring genuine, hands-on, lived-in experience leading Microsoft Fabric implementation and migration engagements — spanning Lakehouse, Warehouse, and OneLake — applying practical, proven strategies to deliver scalable, production-grade unified data platforms.
Lead small engagements or key workstreams within large, complex data and analytics transformations across industriesDesign and implement cloud and hybrid data architectures, migration approaches, and technology solutions across data and analytics platformsManage day-to-day stakeholder relationships and align technical delivery to business and program objectivesOversee quality of team deliverables and recommendations while contributing to hands-on solution delivery across distributed teamsSupport practice growth through proposal development and deal shaping, thought leadership and AI-driven accelerators, and coaching junior practitionersLead delivery of Microsoft Fabric implementation and migration engagements, applying practical, proven architecture and migration patterns across Lakehouse, Warehouse, and OneLake workloadsOwn hands-on build of the Fabric environment and its unified data platform end-to-end across workloads, translating architecture decisions and recommendations into scalable, flexible, working implementationsDesign and build out medallion (bronze/silver/gold) layers in Fabric, and implement role-based access control and data classification definitionsStand up ETL/pipeline work using Spark Notebooks, Dataflow Gen2, Fabric Data Factory with close attention to lineage preservationAdvise across the full breadth of Fabric architecture — workspace organization/structure, CI/CD, data protection planes and best practices, capacity management, backup/DR strategy, and guidance on best fit Fabric itemsStay current with evolving data and analytics technology platform capabilities, proactively evaluating and applying new capabilities to client solutions
6+ years experience with relevant technology project experience in data architecture, data engineering, enterprise architecture, or a related discipline3+ years hands-on experience architecting and delivering end-to-end Microsoft Fabric-based solutions, including lakehouse and warehouse implementations built on Delta Lake table formats, medallion architectures, ingestion pipelines, semantic models, and governance frameworks
Experience implementing Fabric governance and security constructs, including role-based access control (RBAC), data classification, data protection best practices, and integration with Microsoft Purview for enterprise data governanceExperience with dimensional modeling and star schema design in support of semantic models and BI reportingWorking understanding of the latest Microsoft Fabric platform capabilities — such as Fabric IQ, Ontology, Graph, and Real-Time Intelligence — and how to apply them to deliver business value, including grounding agentic AI experiences in enterprise semantic and ontology layersExperience building ETL/pipeline solutions using Fabric Notebooks and/or Dataflow Gen2, with attention to data lineage preservation, and with dbt for transformation orchestration and modeling workflowsExperience with Fabric platform architecture, including workspace organization/structure, CI/CD and deployment pipelines (e.g., Fabric Git integration and deployment pipelines across dev/test/prod), capacity management, and backup/DR strategyExperience supporting deal pursuit and shaping activities, including defining architecture, migration patterns, and implementation approaches4+ years hands-on experience with SQL/T-SQL and Python, including PySpark/Spark SQL for data transformation and analytics modeling, with working knowledge of REST API-based data ingestion and CI/CD practices (e.g., Azure DevOps, GitHub)
4+ years experience leading, managing and delivering complex AI&Data solutions engagementsBachelor’s or Master’s degree in Computer Science, Engineering, Data Science, AI, or related fieldAbility to travel up to 50% on average, based on the work you do and the clients and industries/sectors you serveLimited immigration sponsorship may be available
Experience with Databricks, Snowflake, Google BigQuery and RedshiftExperience in consulting and client-facing delivery rolesExperience in creating critical collaterals for client workshops and customer interactive sessionsExperience presenting technical content to both large and small audiences
To view the associated benefit package, please reference this document Benefit Plan Core/Domain : The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

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