Data Architect (Microsoft Fabric)

NBCUniversal•New York, NY

2d•$120,000 - $140,000•Remote

About The Position

The Data Architect will play a foundational role in designing and implementing Unified Data, a new enterprise data ecosystem for NBCUniversal Operations & Technology. Built primarily on Microsoft Fabric, the platform will establish a centralized, governed data foundation integrating real-time operational events and historical data across enterprise technology domains to support reporting, analytics, automation, AI, and reusable data products. The role defines the target-state architecture, data organization and modeling standards, integration patterns, security and governance framework, and engineering practices. The architect independently evaluates architectural approaches, interdependencies, and technical tradeoffs in a complex and evolving technical environment. The data landscape includes 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. As lead architect and technical leader for the delivery team, the role translates architecture and business objectives into actionable engineering work, helps create and refine epics, stories, and tasks, identifies dependencies and sequencing, routes work to appropriate resources and teams, and provides technical guidance throughout implementation. This is a fully remote position, expected to be performed from a non-NBCUniversal worksite, most commonly the employee’s residence.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent professional experience.
At least 8 years of experience in data engineering, data architecture, solution architecture, or related roles, including responsibility for designing, establishing, or materially evolving enterprise-scale data platforms.
Experience translating architecture and technical strategy into executable roadmaps, epics, stories, tasks, and implementation plans.
Experience serving as a technical lead across multidisciplinary engineering teams or complex technical programs.
Strong experience designing lakehouse and/or warehouse architectures, including layered data patterns from raw ingestion through curated and consumable datasets.
Strong expertise in SQL and proficiency with Python and/or PySpark for data engineering, automation, prototyping, and technical validation.
Experience architecting data solutions on Microsoft Fabric, Microsoft Azure, or comparable enterprise cloud data platforms.
Experience designing both batch and real-time/event-driven data solutions.
Strong understanding of APIs, data integration, distributed data processing, and enterprise integration patterns.
Experience designing secure, scalable, observable, and supportable production data platforms.
Strong understanding of data modeling, metadata, lineage, governance, and data quality principles.
Ability to independently evaluate complex technical tradeoffs involving scalability, reliability, maintainability, security, cost, and business requirements, and determine appropriate architectural approaches.
Strong written and verbal communication skills, including the ability to communicate architectural concepts to engineers, product teams, stakeholders, and senior leadership.
Ability to operate effectively in a greenfield environment where standards, processes, and technical patterns are being established.

Nice To Haves

Experience designing or leading a greenfield enterprise data platform or significant modernization of an existing data ecosystem.
Deep hands-on experience with Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Factory, notebooks, Real-Time Intelligence, Eventstream/Eventhouse, semantic models, and Power BI.
Experience establishing Microsoft Fabric workspace, domain, environment, security, capacity, and deployment strategies.
Experience with Microsoft Azure data technologies and services.
Experience with event streaming and messaging architectures such as Kafka, Azure Event Hubs, Kinesis, or equivalent technologies.
Experience designing change data capture (CDC) and near-real-time ingestion architectures.
Experience with data governance practices and tooling, including cataloging, lineage, classification, data contracts, retention, and privacy controls.
Familiarity with CI/CD and infrastructure automation technologies such as GitHub Actions, Azure DevOps, Terraform, or Bicep.
Experience establishing engineering standards and technical delivery practices across multiple teams.
Experience building platform and pipeline observability, including logging, monitoring, alerting, SLAs/SLOs, and data quality monitoring.
Experience developing architecture decision records, reference architectures, and reusable technical patterns.
Experience designing data foundations that support advanced analytics, machine learning, generative AI, or agentic applications.
Background supporting large-scale Operations & Technology environments such as service operations, workplace technology, infrastructure, endpoint management, identity, security, or network services.
Media, entertainment, or consumer technology experience, particularly where reliability, scale, and cross-domain data integration are critical.
Strong facilitation and presentation skills, including leading technical design discussions and communicating complex architecture decisions to technical and non-technical audiences.

Responsibilities

Independently define the end-to-end Microsoft Fabric architecture, including OneLake, Lakehouse, Warehouse, Data Factory, Real-Time Intelligence, Eventstream/Eventhouse, notebooks, and semantic models.
Establish foundational platform architecture, standards, environment strategy, workspace organization, security boundaries, deployment patterns, and operating principles for a new enterprise data platform.
Translate architecture and program objectives into epics, stories, tasks, dependencies, and implementation sequencing, and help route work to appropriate resources and teams.
Consolidate fragmented operational data into a centralized, discoverable ecosystem using domain-aligned data products, authoritative sources, reusable shared datasets, and common data contracts.
Design operational data solutions across 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 for complex and evolving requirements.
Architect solutions combining streaming and event-driven operational data with historical, transactional, master, and reference data to support operational visibility, analytics, automation, and AI-enabled use cases.
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.
Define enterprise modeling principles, canonical entities, conformed dimensions, shared business definitions, and semantic standards using appropriate dimensional, normalized, domain-oriented, or hybrid approaches.
Define resilient, reusable ingestion, transformation, and orchestration patterns across APIs, databases, SaaS platforms, files, CDC, batch processing, and event streams.
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.
Develop and maintain target-state and reference architectures, architecture decision records, data flow diagrams, technical standards, reusable patterns, and implementation roadmaps.
Establish expectations for source control, CI/CD, environment promotion, infrastructure and configuration automation, testing, monitoring, deployment controls, and release management.
Lead architecture reviews and advise teams on solution designs and major technical decisions to keep implementations aligned with platform architecture, security requirements, performance expectations, and engineering standards.