About the Role:
Data Serving Engineering is building the serving layer for the future that delivers the right data, at the right speed, with the right governance — to every consumer: a T-Mobile customer opening an app, a business analyst asking a natural language question, or an AI agent executing autonomously.
As a Senior Architect, Systems, you will define and own the architectural patterns that make this serving layer scale — how data flows from the Lakehouse to every consumer, which serving stores fit which access patterns, how APIs are governed, and how the stack stays observable, secure, and cost-effective at enterprise scale. We pride ourselves on fostering a culture of innovation, agile ways of working, and transparency in everything we do. Join us in embodying the spirit of the Un-carrier and make a tangible impact!
What You’ll Do:
- Define and own the reference architecture for T-Mobile’s data serving layer — patterns for low-latency serving, caching, streaming, and AI-facing data access.
- Set technical standards for API design, contract governance, and versioning across data-serving APIs consumed by applications and AI agents.
- Architect MLOps and AI serving capabilities including feature serving, vector retrieval, and knowledge graph access for agent-facing use cases.
- Design Infrastructure-as-Code blueprints for provisioning serving stores, sync pipelines, and APIs in a repeatable, governed way.
- Partner with peer architects across Ingest, Data Product, Semantic Layer, Governance, and Platform Engineering to ensure end-to-end coherence.
- Define SLAs, SLOs, and observability standards for data serving systems.
- Lead architecture reviews, evaluate emerging technologies, and recommend platform investments.
- Mentor senior engineers and raise the architectural capability of the team.
What You’ll Bring:
- 10+ years of systems engineering and architecture experience, including data platforms and large-scale distributed systems.
- Proven track record defining reference architectures for data serving, caching, and API layers in enterprise environments.
- Deep expertise in data platform architecture: Lakehouse patterns, Multiple cloud experience
- Strong API design and governance experience: REST, contract-first design, versioning strategy, and consumer onboarding.
- Hands-on knowledge of streaming and caching architectures (Kafka, Redis, CDC, event-driven serving).
- Experience designing MLOps and AI serving infrastructure: feature stores, vector retrieval, and data access for AI agents.
- Familiarity with vector databases and graph databases for AI use cases.
- Proven experience with Infrastructure as Code (Terraform or comparable) and CI/CD frameworks.
- Strong communication skills with the ability to influence senior stakeholders and lead technical decisions across global teams.
Must Have Skills:
- Deep expertise in data platform architecture: Lakehouse patterns, Multiple cloud experience
- Hands-on knowledge of streaming and caching architectures (Kafka, Redis, CDC, event-driven serving).
- API Design and Governance
- Streaming and Caching Architecture
- Infrastructure as Code and CI/CD
Nice-to-Have:
Experience with Knowledge graphs
Designing MLOps platforms or feature serving systems.
MCP-style tooling or agent-facing data access patterns.
Prior architecture leadership in large enterprise environments.
