Description:
The Architect Enterprise Data is a critical role within the Global Data and Analytics team, responsible for owning enterprise semantic model architecture with a primary focus on Finance-aligned analytical models. Mayer Brown’s Data and Analytics team plays a central role in ensuring the accuracy and integrity of enterprise data so it can be used to deliver analytical insights that drive the firm forward. The semantic models this role designs and governs serve as a foundational layer for business intelligence, analytics, and increasingly as the trusted source for Copilot, data agents, and AI-driven delivery across the firm. The role requires designing, governing, and evolving a critical, curated set of high-value semantic models that centralize core financial and operational domains and support broad, reusable analytics use cases across the organization.
Responsibilities
Essential Functions:
- Own the architecture, design, and lifecycle of enterprise semantic models, with an emphasis on Finance-aligned data products
- Partner closely with Finance, Accounting, and Analytics stakeholders to define, standardize, and maintain financial metrics, KPIs, and calculations
- Architect star-schema and dimensional data models optimized for analytical performance and business usability
- Design data architecture blueprints using Microsoft Visio and other tools, creating technical specifications for data flows and integration points
- Centralize related data domains into a limited number of well-governed semantic models to reduce duplication and promote consistency across reporting
- Ensure semantic models support self-service analytics while maintaining metric accuracy, traceability, and governance
- Implement and manage Direct Lake mode semantic models where appropriate to balance performance, freshness, and cost
- Implement and evolve Microsoft Fabric IQ capabilities — including ontology definitions and semantic annotations — to establish trusted context for Copilot, AI agents, and generative AI-driven analytics delivery
- Author and optimize DAX measures and calculations to support complex financial reporting, KPI standardization, and self-service analytics consumption
- Enable seamless consumption of models across Power BI, Excel, and other analytical tools
- Define and govern enterprise modeling standards, naming conventions, and documentation to support enterprise adoption
- Integrate semantic models with Microsoft Purview for data cataloging, lineage tracking, and sensitivity classification to ensure end-to-end governance visibility
- Align data model lifecycle management with governance and access control frameworks through CI/CD pipelines, ensuring promotion, versioning, and policy enforcement are automated and auditable
- Manage semantic model serialization using TMDL (Tabular Model Definition Language) to enable Git-based source control, code review, and automated deployment
- Collaborate with data platform analytics teams to align physical data structures with semantic layer needs
- Advise on workspace and capacity governance within Microsoft Fabric to ensure optimal deployment topology, cost management, and performance isolation
Education/Training/Certifications
Qualifications, Experience and Personal Attributes
- Required: Bachelor’s degree in Information Management, Data Science, Computer Science, or a related discipline.
- Preferred: Master’s degree in Information Management, Data Science, Computer Science, or a related discipline.
- Industry-recognized certifications (preferred):
- Microsoft Certified: Fabric Data Engineer Associate (DP-700)
- Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)
Professional Experience
- 5-7 years of experience in data engineering, Power BI development, semantic model development, or data architecture with demonstrated expertise in designing and implementing enterprise semantic models, preferably in a legal, professional services, or financial services environment with complex data governance requirements
- Experience developing Power BI reports and semantic models for stakeholders for Finance
- Proven experience in cloud-based data engineering using Microsoft Azure, Azure Data Factory, SQL Server, and Microsoft Fabric technologies, including hands-on development of ETL/ELT processes, data transformations, and lakehouse architectures
- Experience with data modeling and architecture design using tools such as Microsoft Visio, implementing star schema, dimensional modeling, and data warehouse design patterns for business intelligence and analytics solutions
- Demonstrated ability to work with cross-functional teams, including data stewards, IT teams, business analysts, and legal professionals to translate business requirements into technical data solutions and documentation
- Preferred experience in multinational environments with exposure to international data privacy regulations (GDPR, CCPA), multi-jurisdictional compliance requirements, and enterprise-scale data governance policies
- Experience with version control systems like Git, CI/CD pipelines, infrastructure as code, and DevOps practices for data engineering workflows and automated deployment processes