Sr Architect, Data [T500-27310]

About T-Mobile: T-Mobile US, Inc. (NASDAQ: TMUS), headquartered in Bellevue, Washington, is America's supercharged Un-carrier, connecting millions through its strong nationwide network and flagship brands, T-Mobile and Metro by T-Mobile. Customers benefit from an unmatched combination of value, quality, and exceptional service experience.

TMUS Global Solutions: TMUS Global Solutions is a world-class technology powerhouse accelerating the company's global digital transformation. With a culture built on growth, inclusivity, and global collaboration, the teams here drive innovation at scale, powered by bold thinking.

Sr Data Architect — Enterprise Data Platforms About the Role: As a Data Architect, you will design and build the data architecture for one or more customer domains within a multi-squad data engineering practice serving Critical Infrastructure, Magenta Service Center, and Credit Risk & Fraud. You will report into the US-based team and work directly with the Tech Lead, Senior Data Engineers, Technical Product Managers, and Senior BI Analysts — turning customer outcomes into well-designed data products on Databricks Lakehouse, Snowflake CDW, and Microsoft Fabric. This is a hands-on architect role focused on design and delivery. You will produce the pipeline designs, data models, integration patterns, and migration plans your squad builds against — and you'll be in the code on the harder pieces of the work, not just on the whiteboard. You will partner with the Tech Lead on platform-level standards and decisions, but the day-to-day focus is owning the architecture for your squad's data products and helping the engineers on your squad ship them well. We pride ourselves on encouraging a culture of innovation, agile ways of working, and transparency in all we do. Join us in embodying the spirit of the Un-carrier and make a tangible impact.

What You'll Do:

• Design end-to-end data architecture for your squad's data products — including Databricks Lakehouse medallion design (Bronze / Silver / Gold), Snowflake CDW models, Unity Catalog structure, and the integration patterns that connect source systems to consumption layers.
• Produce the technical designs for major pipeline builds: ingestion patterns, transformation logic, schema design, partitioning and performance choices, data quality controls, and operational considerations.
• Contribute code on the harder pieces — complex transformations, performance-critical pipelines, integration scaffolding, reference implementations the rest of the squad builds from.
• Lead design reviews for your squad's deliverables; identify design risks before they reach production and propose alternatives that are both pragmatic and durable.
• Partner with the Tech Lead and other architects on platform-level patterns — ingestion frameworks (including the Unified Ingestion Framework), streaming standards, semantic-layer design, data product certification criteria — and apply those patterns consistently in your squad's work.
• Design and lead complex legacy migrations: SQL Server Lakehouse, Alteryx Databricks, on-prem Oracle / SAP Lakehouse, Snowflake Lakehouse interop — from extraction through validation, rebuild, and decommission.
• Partner with the Real-Time Integration Engineering team on event-driven design for streaming use cases (Kafka, Azure Event Hub, Delta Live Tables) when your squad needs them.
• Partner with the enterprise Semantic Layer team on per-domain semantic model design in Microsoft Fabric — measures, dimensions, deduplication with adjacent domains, and certification readiness.
• Translate architecture decisions into terms that land with Technical Product Managers, customer-side stakeholders, and non-engineers — so design choices are understood, not just shipped.
• Apply enterprise data standards by default: data classification (including USGCI), encryption of sensitive elements, access controls, audit logging, SOX-controlled data flows where applicable, and AI-ready data foundations.
• Internalize the customer context behind your squad's work and make design decisions consistent with it; choose the fastest responsible path to meet the immediate need, then decide what should become a durable, reusable pattern.
• Drive clarity in ambiguity — surface design risks early, name the decisions to be made, identify owners, and propose a path forward across cross-team dependencies.
• Mentor Senior and mid-level data engineers on the squad — through design reviews, pair-design sessions, and direct code feedback.

What You'll Bring:

• 7–10 years of data engineering or data platform engineering experience, with at least 2 years in a data architect, data modeler, or senior engineer capacity owning end-to-end designs.
• Strong hands-on Databricks experience: Delta Lake, Unity Catalog, Databricks Workflows, Structured Streaming, and performance tuning.
• Working experience with Microsoft Fabric — OneLake, Fabric Data Engineering, and exposure to Fabric IQ semantic layer design.
• Snowflake CDW experience — warehouse design, performance tuning, and a working understanding of Snowflake / Databricks (Iceberg) interop patterns.
• Experience with real-time streaming architectures (Kafka, Azure Event Hub, Delta Live Tables, or comparable) — at least at the design and integration level.
• Strong proficiency in PySpark and Spark SQL for distributed data transformation and pipeline design.
• Experience designing ERP or enterprise-system ingestion pipelines, with familiarity with Change Data Capture (CDC) patterns.
• Experience designing or leading legacy platform migrations — extraction, validation, rebuild, and decommission.
• Fluency in cross-functional conversations — able to walk a Technical Product Manager, customer stakeholder, or non-engineer through a design decision and have it land.
• Customer-minded engineering builds with the downstream consumer and business outcome in mind; uses sound judgment on speed vs. durability.
• Drives clarity in ambiguity — surfaces design risks early, names decisions, identifies owners, and proposes a path forward.
• Pragmatic, direct communication — clear and concise in design docs, reviews, and stand-ups; able to explain technical decisions to non-engineers.
• Agile delivery experience (Jira, Confluence, CI/CD).

Must Have Skills:

• Azure Data Factory, Azure

Databricks , Microsoft Fabric, Delta Lake, SQL,

Spark, PySpark, Python , Snowflake ,

DBT(Data Build Tools)

• Real time

Streaming Pipelines (Kafka/Azure Event Hub/Deepio)

• Data Modelling, ETL/ELT Pipelines, Scheduling Tools(like Control-M, Autosys etc.), Unity Catalog, RBAC
• CI/CD,

GITLAB , System Designing, Cloud Data Architectures, Data Governance, Optimizations and Performance Tuning

Nice-to-Have:

• Azure certifications (DP-203, AZ-900, DP-900),
• Agile / Scrum
• AI Tools(Claude, Co-pilot etc.) & AI/ML-ready data preparation
• Familiarity with USGCI, SOX
• Alteryx/PowerBI
• Background in Finance, Procurement, Network Infrastructure, or Credit Risk data domains