Data Architect (Microsoft Fabric)

As a Data Architect, you will play a foundational role in the design and implementation of Unified Data, a net-new, greenfield enterprise data ecosystem for NBCUniversal Operations & Technology. Built primarily on Microsoft Fabric, Unified Data will establish a centralized, governed data foundation that brings together real-time operational events and historical data across enterprise technology domains to support reporting, analytics, automation, AI, and reusable data products. You will define the target-state architecture, data organization and modeling standards, integration patterns, security and governance framework, and engineering practices for the platform. Operating within a complex and evolving technical environment, you will exercise independent judgment to evaluate multiple architectural approaches, assess interdependencies and technical tradeoffs, and determine solutions where established patterns or precedent may be limited. The data landscape spans areas including IT service management, employee and workplace technology, endpoints and assets, identity and access, infrastructure and network services, collaboration platforms, operational telemetry, and enterprise master and reference data. In addition to serving as the lead architect, you will act as a technical leader for the delivery team, providing guidance and translating architecture and business requirements into actionable engineering work, helping create and refine epics, stories, and tasks, identifying dependencies and sequencing, and ensuring work is aligned to the appropriate resources and teams while providing technical guidance throughout implementation. Responsibilities: Microsoft Fabric Architecture & Strategy: Independently define the end-to-end architecture for Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Factory, Real-Time Intelligence, Eventstream/Eventhouse, notebooks, and semantic models to support cross-domain Operations & Technology use cases. Greenfield Platform Architecture: Develop and establish the foundational architecture, standards, environment strategy, workspace organization, security boundaries, deployment patterns, and operating principles for a net-new enterprise data platform. Technical Leadership & Delivery Enablement: Serve as the technical leader for the delivery team by translating architecture and program objectives into epics, stories, tasks, dependencies, and implementation sequencing while helping route work to the appropriate resources and teams. Enterprise Data Centralization: Drive the consolidation of fragmented operational data into a centralized and discoverable ecosystem using domain-aligned data products, authoritative data sources, reusable shared datasets, and common data contracts. Operational Data Architecture: Design solutions spanning IT service management, workplace technology, endpoints and assets, identity, infrastructure, networks, collaboration platforms, telemetry, events, logs, and enterprise reference data. Evaluate cross-domain dependencies and technical tradeoffs to determine architectural approaches that address complex and evolving requirements. Real-Time & Historical Data Design: Architect solutions that combine streaming and event-driven operational data with historical, transactional, master, and reference data to support operational visibility, analytics, automation, and AI-enabled use cases. Lakehouse & Warehouse Patterns: Establish scalable patterns for raw, enriched, and curated data layers, Delta-based lakehouse designs, warehouse workloads, OneLake shortcuts, virtualization, partitioning, optimization, retention, and performance management. Data Modeling & Semantic Standards: Define enterprise modeling principles, canonical entities, conformed dimensions, shared business definitions, and semantic standards using dimensional, normalized, domain-oriented, or hybrid approaches as appropriate. Integration & Pipeline Architecture: Define resilient and reusable ingestion, transformation, and orchestration patterns across APIs, databases, SaaS platforms, files, CDC, batch processing, and event streams. Data Governance, Security & Quality: Partner with security, privacy, and governance teams to establish access controls, metadata, lineage, classification, data ownership, data contracts, quality standards, retention policies, and lifecycle controls. Architecture Standards & Documentation: Independently develop and maintain target-state architectures, reference architectures, architecture decision records, data flow diagrams, technical standards, reusable patterns, and implementation roadmaps. DevOps & Engineering Standards: Establish expectations for source control, CI/CD, environment promotion, infrastructure and configuration automation, testing, monitoring, deployment controls, and release management. Architecture Review & Technical Governance: Lead reviews and advise teams on solution designs and major technical decisions to ensure implementations remain aligned with Unified Data architecture, security requirements, performance expectations, and engineering standards.