Principal Data Engineering Architect

** Overview*
* Effective marketing starts with understanding customers: how they engage, what they need, and which experiences create value.  
** Microsoft's Marketing Data Engineering Team
** turns signals across web, campaigns, content, seller and partner interactions, and sales outcomes into trusted data products. We help marketers improve multi-channel customer engagement and learn faster through evidence, with AI-native self-service that makes trusted data easier to find and use.

As a  
** Principal Data Engineering Architect** , you will lead the architecture and hands-on development of petabyte-scale systems processing billions of events per day that power the business. You will help shape team strategy across data architecture, modeling, solution design and data products, and provide technical direction for major capabilities. This individual contributor role combines autonomy with influence across partner engineering teams, enabling domain teams through shared standards.

Business acumen is central to the role. You will work closely with marketers, marketing technologists, analysts and data scientists to understand their challenges and the why behind requests. Connect customer and business needs to reusable capabilities that solve more than a single requirement, while choosing the simplest approach that delivers value without overbuilding.

Microsoft's mission is to empower every person and every organization on the planet to achieve more. We bring a growth mindset to our work, collaborate across boundaries, and build on the values of respect, integrity, and accountability to create an inclusive culture where everyone can thrive.

** Join us to unleash your potential
** and play a pivotal role as a data innovator with world-changing impact - your journey starts here!

** Responsibilities*
* ** Business Understanding & Impact*
* This role enables Microsoft's Marketing organization to transform raw data into trusted, scalable, AI-ready data products that fuel campaign performance measurement, customer intelligence, audience engagement, revenue growth, and executive decision-making through the Microsoft Fabric ecosystem.

The  
** Principal Data Engineering Architect
** is responsible for the following:

+ Partner with PMs and business users to define problems, connect requests to customer journeys and business outcomes, and prioritize reusable capabilities. Use feedback, adoption, reuse and quality measures to guide product decisions and team data strategy.

+ Lead solution design and production implementation of major data capabilities. Build hands-on prototypes, contribute production code, review code, models and designs, and remain accountable for operational reliability and measurable product outcomes.

+ Develop logical, physical and semantic models for foundational marketing domains. Productize engagement signals with clear business definitions, metric logic, contracts and ownership so analysts, applications and AI consumers can use data without guessing its meaning.

+ Design distributed processing and storage using streaming, batch or combined patterns. Apply immutable event data and flexible schema evolution to protect downstream products. Make explicit tradeoffs among latency, accuracy, reliability, cost and maintainability.

+ Influence engineering and user-facing tools for governed self-service ingestion, publishing and extension of platform data. Improve discovery, access and user experience through interoperable templates, interfaces and semantic abstractions that preserve domain ownership.

+ Embed security, privacy and quality by design, including metadata, lineage, classification, policy-aware access and retention. Establish monitoring and engineering practices that make federated products trustworthy, supportable and cost-conscious throughout their lifecycle.

+ Advance AI automation and governed code generation for transformations, tests, documentation and self-service data extension. Use bounded assistance for operational detection and recommendations, with rigorous validation, approved remediation and human judgment for consequential changes. Measure delivery and operational improvements.

+ Bring a startup mindset to a large, complex data environment: listen, simplify, iterate quickly and make sound decisions. Mentor and coach engineers, strengthen reviews and standards, and align partner teams through technical credibility and collaboration.

** Other*
* + Embody our culture ()  and values. ()

** Qualifications*
* *
* Minimum…