Sr Manager, Digital Products & Platforms

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Job Overview
This role leads engineering teams that design, build, and operate enterprise-grade applications and digital products within an Agile, automation-first delivery model. The teams work across the full stack—Java/Spring Boot, Angular/React, Python, APIs, and data-enabled platforms—and increasingly leverage agentic AI workflows (autonomous code generation, AI-assisted testing, intelligent pipeline orchestration) to multiply velocity and reduce manual toil. This is a people-leadership role with an engineering backbone—the Sr Manager sets technical direction, makes architecture trade-off decisions, coaches engineers, and holds the quality bar, but spends the majority of time leading teams rather than writing production code. Success is measured by delivery throughput, team health and retention, engineering cycle time reduction, and the ability to run a lean organization that ships more with less through smart automation. The work directly impacts T-Mobile’s product velocity, customer experience, and operational scalability..

Job Responsibilities

• Lead and manage cross-functional engineering teams (software engineers, QA, DevOps, data engineers, product managers) to deliver enterprise applications—providing technical direction, architecture guidance, coaching, and performance management
• Build and evolve an agentic AI capability within the engineering org—standing up AI-assisted workflows (automated code generation, test creation, Jira-to-MR pipelines) that reduce cycle time and free engineers for high-judgment work
• Define and drive continuous improvement and engineering excellence—CI/CD pipeline optimization, automated quality gates, tech debt management, and measurable productivity frameworks (DORA metrics, automation coverage)
• Own the technical roadmap in collaboration with architecture, platform engineering, cybersecurity, and design teams—balancing feature delivery, platform modernization, and automation investment
• Manage technical vendor relationships and evaluate emerging technology (AI/ML tooling, cloud-native platforms, data infrastructure) to keep the engineering stack current and competitive
• Recruit, hire, and develop engineering talent who bring strong full-stack fundamentals, a bias toward automation, and the ability to work effectively alongside AI-augmented toolchains

Education and Work Experience

• Bachelor's Degree and 7 years of related work experience or a combination of education and experience deemed equivalent Acceptable areas of study include Computer Science, Engineering, IT or equivalent experience. (Required)
• 7-10 years experience of developing large scale business systems applications. in an agile product development environment with an engineering background across the modern stack (Java, Python, JavaScript/TypeScript, APIs, databases)
• 2-4 years People leadership managing cross-functional engineering teams (5+ engineers in direct reporting relationships)—including hiring, performance management, career development, and org design((Required)
• 2-4 years applying AI/ML concepts or LLM-based tools in production environments; able to evaluate architectures and lead AI-augmented delivery (Preferred)
• 2+ years driving delivery excellence through process improvement, automation, and measurable gains in velocity (e.g., cycle time, deployment frequency, reduced toil))(preferred)
• 2+ years working with big data platforms (e.g., Snowflake, Databricks, Spark, Kafka) and data pipeline operations (Preferred)
• 1+ year operating in cloud-native/Kubernetes environments with CI/CD and infrastructure-as-code(Preferred)
• 1+ year experience with API platforms, gateways, or developer ecosystems(Preferred)

Preferred Knowledge, Skills and Abilities

• Engineering Leadership & Talent Development
  Sets technical direction, makes sound architecture decisions, and leads design/code reviews. Builds and develops high-performing teams through strong hiring, coaching, and career growth practices.
• Full-Stack Fluency
  Working knowledge of modern application architectures (backend services, frontends, APIs, and data stores) sufficient to evaluate designs, unblock teams, and guide technical decisions.
• AI &