Sr Engineers, Data

T-Mobile•Atlanta, GA

20h•$164,736 - $169,700•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! Position summary 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 Atlanta, GA will develop data engineering solutions, including data pipelines, visualization, and analytical tools.

Requirements

Architect, design, and implement scalable enterprise data platforms across on-premises, cloud, and hybrid environments.
Develop modern data architectures leveraging Microsoft Fabric, Azure Data Lake (ADLS), Snowflake, PostgreSQL, and MS SQL Server to support analytics, reporting, and AI initiatives.
Leverage Azure Databricks to develop scalable distributed data transformation, cleansing, and feature engineering processes.
Productionize notebooks into automated jobs using CI/CD pipelines to deploy enterprise AI/ML solutions.
Design and develop end-to-end ETL/ELT pipelines using Python-based scripting and distributed processing frameworks.
Build robust workflows to ingest, cleanse, transform, and integrate structured, semi-structured, and unstructured data from heterogeneous sources into operational data stores and enterprise data warehouses.
Develop high-performance data models and optimize SQL queries, indexing strategies, partitioning, and stored procedures to ensure scalability, performance, and data integrity across large-scale datasets.
Build predictive analytics and machine learning solutions using Python and prepare feature-engineered datasets and support model training, testing, and deployment pipelines.
Implement DevOps and CI/CD best practices using GitLab, including repository management, version control strategies, automated testing, pipeline configuration, and deployment of data engineering and ML workloads.

Responsibilities

Design and implement scalable data architectures across on-premises and cloud environments, building core platform components, defining integration patterns, and ensuring performance, security, and governance standards are consistently applied.
Execute advanced performance tuning and optimization, including SQL query refactoring, indexing, and partitioning strategies, Spark job optimization, and workload management to support high-volume, mission-critical data systems.
Collaborate with product owners and business stakeholders to translate functional requirements into technical designs, detailed task breakdowns, and scalable implementation plans, while delivering core development components.
Support production operations and incident resolution for data platforms, proactively monitoring pipelines and workloads, troubleshooting performance bottlenecks, and implementing enhancements to improve data processing.
Develop complex data wrangling and exploratory workflows across relational, semi-structured, and streaming data sources, implementing scalable transformation logic and optimizing ingestion pipelines to deliver reliable, analytics-ready datasets.
Implement and maintain data governance, quality, and monitoring frameworks, including data validation rules, logging mechanisms, metadata management, and lineage tracking to ensure data accuracy, auditability, and compliance with enterprise standards.
Provide hands-on mentorship and technical guidance to team members, supporting skill development in ETL/ELT design, distributed processing, cloud data engineering, and DevOps automation.
Promote engineering best practices through active participation in design reviews, code reviews, and CI/CD pipeline implementation, ensuring high-quality, testable, and production-ready data and ML deployments.
Create and maintain technical documentation, architecture diagrams, and deployment standards, supporting knowledge sharing, maintainability, and operational continuity.