Sr Engineers, Software

T-Mobile•Frisco, TX

2d•Hybrid

About The Position

Sr. Engineers, Software located in Frisco, TX, will create and maintain optimal data pipeline architecture and assemble large, complex data sets that meet functional and non-functional business requirements. 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.

Requirements

Developing, optimizing, and troubleshooting complex SQL queries across Snowflake, Teradata, Oracle, and SQL Server. Working with advanced techniques including multi-join operations, common table expressions (CTEs), window functions, recursive queries, and performance tuning for large-scale datasets.
Applying SQL expertise to real-world use cases in the wireless industry, with a focus on financial reconciliation, KPI measurement, and data-driven decision support.
Designing and building enterprise-grade datasets, data marts, and data warehouses across both on-premises and cloud platforms. Strong foundation in dimensional modeling, normalization and denormalization, and modern layered/Medallion architectures.
Aligning data solutions to reporting needs, KPI visualization, and business modeling requirements within financial and operational domains, ensuring scalability, accuracy, and business value.
Developing end-to-end data pipelines that enable scalable ingestion, transformation, and extraction across diverse environments. Leveraging Python, Scala, Java, and JavaScript to build reliable workflows, integrate APIs, and automate complex processes.
Applying orchestration frameworks and reusable components that embed data validation and reconciliation, ensuring accuracy and consistency.
Engineering pipeline solutions that support both operational and analytical workloads, with a focus on Finance and Operations use cases.
Using Agile development practices, Jira, Jira Align, and Git to manage delivery, collaboration, and version control.
Refining requirements, managing backlogs, and aligning development activities with data engineering objectives while championing iterative development, transparency, and continuous improvement to deliver reliable and scalable solutions.
Performing root cause analysis across large and complex datasets for identifying and resolving data issues, enhance accuracy, and address critical business questions.
Applying data quality frameworks, financial reconciliation processes, and rigorous validation controls to safeguard reporting integrity and ensure trusted insights for decisionmaking.
Leveraging Azure Databricks, Azure Data Lake Storage (ADLS), and Azure Data Factory for designing, orchestrating, and optimizing end-to-end data pipelines.
Building scalable, cloud-native data warehouses and data marts that power KPI dashboards, financial reporting, and operational analytics.
Strong knowledge of performance optimization, governance, and data lineage practices within the Azure ecosystem for ensuring reliability, security, and compliance.

Responsibilities

Assemble large, complex data sets that meet functional and non-functional business requirements.
Identify, design, and implement internal process improvements, including automating manual processes, optimizing data delivery, and re-designing infrastructure for greater scalability.
Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AZURE technologies.
Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
Work with stakeholders including the executive, product, data, and design teams to assist with data-related technical issues and support their data infrastructure needs.
Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
Work with data and analytics experts to strive for greater functionality in our data systems.