Job Description & How to Apply Below
Sr. Data Engineer
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.
About 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.
About the Role:
The Data Engineer (Level III) designs, builds, and operates the data pipelines and lakehouse data products that power P&SC intelligence across device supply chain, reverse logistics, procurement, network supply chain, and real-time control tower capabilities. Operating within a squad focused on a specific SC domain or cross-cutting platform function, the Data Engineer is a hands-on technical contributor who builds to production quality, owns pipeline reliability and KTLO, and contributes to the engineering standard of the team.
What You’ll Do:
Design and build production-grade data pipelines spanning source ingestion, bronze landing, silver transformation, and gold-layer data product delivery within the squad's domain scope.
Own pipeline KTLO (Keep the Lights On) for assigned data products, including monitoring, alerting, incident response, and ongoing reliability improvements.
Implement data ingestion patterns for assigned source systems including batch file ingestion, API-based ingestion, and event-driven streaming (Kafka, Azure Event Hub) depending on squad scope.
Apply medallion architecture (bronze, silver, gold) and Fabric IQ certification standards consistently across all data product builds.
Collaborate with System Analysts to implement field-level transformations, business rule logic, and data quality checks as specified in product requirements documentation.
Participate in and contribute to pipeline design reviews, ensuring solutions align with the organization's Databricks and Fabric engineering standards.
Support the migration and deprecation of legacy platforms including SCOpsBI SQL Server and SAP boundary systems, following the organization's extract, validate, rebuild, cutover, and decommission pattern.
Write and maintain comprehensive pipeline documentation including data lineage, transformation logic, SLA definitions, and dependency maps.
Contribute to the organization's Dev Ops and engineering reliability practices including CI/CD pipeline setup, testing frameworks, and incident runbooks.
Support and collaborate with junior data engineers within the squad, sharing knowledge and providing guidance on day-to-day engineering tasks.
Must Have
Skills:
5-7 years of data engineering experience with a track record delivering production data pipelines in enterprise environments.
Expert proficiency in PySpark or Spark SQL for large-scale data transformation on distributed compute platforms.
Hands-on experience with Databricks including Delta Lake, Unity Catalog, and workflow orchestration.
Experience with Microsoft Fabric or Azure Synapse Analytics; familiarity with Fabric IQ and One Lake is a plus.
Proficiency with pipeline orchestration tools such as Azure Data Factory, Databricks Workflows , or Apache Airflow .
Solid SQL skills for data validation, transformation logic, and ad-hoc source system analysis.
Experience building and maintaining ingestion pipelines from enterprise operational systems such as ERP, WMS, TMS, or comparable platforms .
Strong understanding of data quality frameworks, including implementing checks, alerting on anomalies, and maintaining SLA-compliant pipeline health.
Experience with Dev Ops practices for data pipelines including version control (Git), CI/CD , and automated testing.
Ability to operate with moderate independence on complex technical problems, escalating appropriately and maintaining clear technical documentation.
Nice to Have:
Experience with real-time streaming technologies including Kafka, Azure Event Hub, Delta Live Tables, or Spark Structured Streaming.
Familiarity with Oracle, ERP ingestion patterns, or large-scale ERP migration programs.
Background in supply chain, logistics, reverse logistics, or procurement data domains.
Experience with legacy platform migration and decommission programs.
Exposure to T-Mobile's Omni lakehouse platform or Magenta Built integrations.
Python development skills beyond PySpark, including utility scripting and framework contributions.
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