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Job Description & How to Apply Below
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.
TMUS India Private Limited operates as TMUS Global Solutions.
Sr. Engineer, Data – Data Ops (L08)
About the Role:
The Sr. Engineer, Data is responsible for designing, developing, operating, and supporting scalable data architectures and ingestion pipelines across on-premises, cloud, and hybrid environments to meet enterprise data and analytics needs. The role partners with data engineers and cross-functional teams to architect, build, deploy, and support data warehouse and business analytics solutions in a highly dynamic 24x7 operational environment. Key responsibilities include operating and optimizing complex data pipelines, ensuring reliable data delivery, automating manual and operational processes using Python, Spark, Scala, Databricks, SQL, Snowflake, and Azure, and supporting platform health, compliance, and incident response.
As a core member of T-Mobile’s Data Operations Center of Excellence, the Sr. Engineer drives operational excellence, reliability, and continuous improvement while mentoring team members and strengthening data engineering and operational maturity.
What You’ll Do:
What You’ll Bring :
Must Have
Skills:
Nice to Have:
Design and develop data engineering solutions that enable data ingestion pipelines, visualization, and analytical tools to meet business requirements
Architect and implement scalable data platforms across on-premises, cloud, and hybrid environments
Build and optimize data pipelines to improve data delivery, reliability, and performance
Automate manual processes using Python, Spark, Databricks, SQL, and cloud technologies
Mentor and guide team members to strengthen data engineering skills and professional growth
Contribute to team knowledge sharing and advancement of data engineering standards
Support project definition activities including estimation, planning, and scoping
Foster a collaborative, influence-driven team culture with high individual accountability and technical depth
Bachelor’s degree in computer science, Computer Engineering, or a related field, or equivalent practical experience
5–7 years of hands-on experience designing, building, and supporting data engineering and ingestion solutions
Strong experience developing and migrating data solutions across cloud platforms (Azure, AWS, GCP)
Demonstrated experience automating data pipelines using Python and modern cloud-native technologies
Strong analytical and problem-solving skills applied to complex data ingestion and integration challenges
Ability to manage multiple work streams with strong organizational and prioritization skills
Passion for learning and applying new data engineering and platform technologies
Experience working in collaborative, cross-functional teams with high accountability
Experience designing, building, and optimizing data ingestion pipelines using Python and SQL
Hands-on experience with cloud-native data platforms and services, including ADF, ADLS , Databricks, and Snowflake
Experience triaging and remediating code and data issues in complex ETL/ELT pipelines
Familiarity with CI/CD and Dev Ops practices, including Git, pipelines, automated testing, and deployments
Experience with SQL, No
SQL, Vector, and relational database design and development
Proficiency in data pipeline development using Python and experience with languages such as DAX, Java, Scala, and/or Go
Working knowledge of message queuing, stream processing, and scalable big-data data stores
Ability to manage and support batch and streaming data pipelines across enterprise and cloud platforms
Cloud or data platform…
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