Associate Data Engineer: Cloud Pipelines & Insights

T-Mobile; Overland Park (KS); On-site; USD 65,000 - 129,000; Full time

Job summary

T-Mobile is seeking a Data Engineer to support the development of infrastructure for efficient extraction, transformation, and loading of data from diverse sources using SQL and cloud technologies. It primarily involves designing and implementing components to improve internal data processes and infrastructure scalability.

The role focuses on automation, optimization, and re-design of data delivery systems to enhance performance, accessibility, and insights for decision-making across the

Qualifications

• Bachelor's degree or equivalent required.
• Experience with SQL, Python, Java, Scala, or Go preferred.
• 2+ years in data engineering preferred.

Responsibilities

• Develop and maintain data pipeline architecture and analytical tools to meet business requirements and improve data processing efficiency.
• Assemble and prepare data sets that fulfill both functional and non-functional business needs for analysis.
• Build components of analytics tools that leverage data pipelines to generate actionable business insights.
• Collaborate with stakeholders to resolve data-related technical issues and support data infrastructure requirements.
• Create data tool components to support analytics and data science teams in their workflows.
• Work with data and analytics experts to enhance the functionality and performance of data systems.
• Also responsible for other duties/projects as assigned by business management as needed.

Skills

Data pipelines

Data Engineering

Data Analysis

Cloud Computing

Collaboration

Databricks DBRX

Microsoft Azure

SQL

Python

Go

Education

Bachelor's Degree or equivalent

Tools

Databricks DBRX

Microsoft Azure

Job description

T-Mobile is seeking a Data Engineer to support the development of infrastructure for efficient extraction, transformation, and loading of data from diverse sources using SQL and cloud technologies. It primarily involves designing and implementing components to improve internal data processes and infrastructure scalability.

The role focuses on automation, optimization, and re-design of data delivery systems to enhance performance, accessibility, and insights for decision-making across the