Sr Engineers, Data

T-Mobile•Frisco, TX

6d•$156,998 - $164,300•Hybrid

About The Position

At T-Mobile, we invest in YOU! Our Total Rewards Package ensures that employees get the same big love we give our customers. All team members receive a competitive base salary and compensation package - this is Total Rewards. Employees enjoy multiple wealth-building opportunities through our annual stock grant, employee stock purchase plan, 401(k), and access to free, year-round money coaches. That’s how we’re UNSTOPPABLE for our employees! T-Mobile is America’s supercharged Un-carrier, delivering an advanced 4G LTE and transformative nationwide 5G network that will offer reliable connectivity for all. Sr Engineers, Data located in Frisco, TX, will serve as the Single-Point-of-Contact (SPOC) for all data, automation, and reporting infrastructure. Position duties and responsibilities include, but are not limited to: Architect and implement robust data pipelines and systems that support large-scale ingestion, transformation, and delivery of data across hybrid cloud environments. Evolve the current infrastructure to be more modular, cloud-native, and ML-ready, ensuring high availability and performance across Wholesale analytics needs. Act as the technical lead and SME for AI/ML technologies within Wholesale, influencing the roadmap by identifying opportunities where machine learning can drive measurable business value. Design and support pipelines that facilitate the deployment and monitoring of ML models in production in collaboration with data science partners. Contribute to the governance and ethical application of AI, helping define best practices for responsible ML usage across the organization. Mentor junior engineers and analysts in the Wholesale organization on cloud-native architecture, and advanced analytics. Partner with other teams to ensure alignment with enterprise standards and to facilitate data democratization across the business. Continue to serve as Wholesale’s liaison to T-Mobile’s ETS, Security, Product Owners, and external vendors such as Snowflake, Salesforce, Alteryx, AWS, and Microsoft. Shape data governance and technical standards in partnership with stakeholders to maintain secure and compliant data environments. Lead the design and deployment of reusable frameworks for real-time and batch data processing using technologies such as Spark, Python, SQL, and Azure Data Factory. Drive initiatives to automate repetitive tasks, enhance data quality, and streamline reporting/analytics for end-users. Collaborate with senior business leaders to translate strategic objectives into data initiatives, and support long-term roadmapping of data and AI capabilities within Wholesale. Explore emerging data technologies, including generative AI, feature stores, and ML Ops, that can shape the next generation of Wholesale data systems. Telecommuting is permitted, but applicant must work from the worksite location at least 3-4 days per week. No additional national or international travel is anticipated.

Requirements

Architecting and optimizing cross-platform ETL frameworks spanning Snowflake, SQL Server, Azure, Netcracker, Databricks, and Google Analytics, enabling robust, scalable data ecosystems that power advanced analytics and AI workloads.
Designing and implementing intelligent automation strategies using tools that include Alteryx, Power Automate, and Snowflake schedulers to streamline complex data flows, eliminate redundancy, and strengthen enterprise data reliability.
Acting as a strategic data subject matter expert, stewarding integrated data lab environments in Snowflake and SQL Server, and leading unified data ingestion efforts that promote a centralized and governed analytics infrastructure.
Being a champion for data-driven storytelling through Power BI, including advanced Gateway configuration, self-service enablement, and integration of AI-assisted insights via Microsoft Copilot and Snowflake Cortex to accelerate business intelligence delivery.
Leading innovation in secure virtualized analytics environments by managing Polaris-based vRA systems to ensure cybersecurity compliance and maximize remote analytics capabilities.
Advancing next-gen analytics capabilities by integrating AI into reporting workflows using Microsoft Copilot and OpenAI APIs, enabling AI-assisted querying, model training, and scalable data augmentation for future-ready insights.
Master’s degree in Data Science or related, and 3 year of relevant work experience.
Bachelor’s degree in Data Science or related, and 5 years of relevant work experience.
At least 18 years of age
Legally authorized to work in the United States

Responsibilities

Serve as the Single-Point-of-Contact (SPOC) for all data, automation, and reporting infrastructure.
Architect and implement robust data pipelines and systems that support large-scale ingestion, transformation, and delivery of data across hybrid cloud environments.
Evolve the current infrastructure to be more modular, cloud-native, and ML-ready, ensuring high availability and performance across Wholesale analytics needs.
Act as the technical lead and SME for AI/ML technologies within Wholesale, influencing the roadmap by identifying opportunities where machine learning can drive measurable business value.
Design and support pipelines that facilitate the deployment and monitoring of ML models in production in collaboration with data science partners.
Contribute to the governance and ethical application of AI, helping define best practices for responsible ML usage across the organization.
Mentor junior engineers and analysts in the Wholesale organization on cloud-native architecture, and advanced analytics.
Partner with other teams to ensure alignment with enterprise standards and to facilitate data democratization across the business.
Serve as Wholesale’s liaison to T-Mobile’s ETS, Security, Product Owners, and external vendors such as Snowflake, Salesforce, Alteryx, AWS, and Microsoft.
Shape data governance and technical standards in partnership with stakeholders to maintain secure and compliant data environments.
Lead the design and deployment of reusable frameworks for real-time and batch data processing using technologies such as Spark, Python, SQL, and Azure Data Factory.
Drive initiatives to automate repetitive tasks, enhance data quality, and streamline reporting/analytics for end-users.
Collaborate with senior business leaders to translate strategic objectives into data initiatives, and support long-term roadmapping of data and AI capabilities within Wholesale.
Explore emerging data technologies, including generative AI, feature stores, and ML Ops, that can shape the next generation of Wholesale data systems.