This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
This role is essential for designing and developing data architectures across on-premise, cloud, and hybrid platforms to support organizational data needs. It primarily involves collaborating with data engineers to analyze, architect, design, and develop data warehouse and business analytics solutions. The role includes mentoring team members to enhance their data engineering skills and capabilities. Success is measured by the effectiveness of data engineering solutions, team skill development, and contribution to data architecture innovation. The work impacts the organization by enabling data-driven decision-making and advancing data infrastructure capabilities for business insights. T-Mobile’s Data & Intelligence Center of Excellence supports the modernization of Finance data platforms across planning and forecasting, revenue accounting, tax compliance, treasury operations, and related reporting functions. In this role, you will help build governed, reliable data pipelines and curated Finance data products using a modern Azure Data Lake Storage and Databricks, DBT lakehouse environment. You will work with architects, product managers, Finance domain experts, and engineering partners to migrate legacy processes, improve data quality, and deliver trusted data for analytics, operational reporting, and compliance-sensitive business processes.
Job Responsibility
Develop data engineering solutions that enable data pipelines, visualization, analytical tools and AI platforms to support business requirements
Design and develop data architectures across on-premise, cloud, and hybrid platforms to ensure scalable data infrastructure
Build and maintain ingestion pipelines from Finance source systems, including ERP platforms, relational databases, flat files, cloud data warehouses, and API-based feeds, into governed cloud data lake environments
Develop and maintain transformation logic and reusable data models using SQL, Python, Databricks, dbt, and related data engineering tools
Perform data wrangling, exploration, and discovery of heterogeneous data to generate new business insights
Support migration of legacy ETL, batch, and database-driven Finance processes into modern stack
Implement data quality checks, validation rules, monitoring, and reconciliation processes to improve accuracy, completeness, lineage, and timeliness of published data
Contribute to SOX-sensitive and audit-aware data engineering practices, including peer review, version control, change traceability, testing, and reproducible data processing where applicable
Partner with data architects, product managers, Finance domain teams, and engineering peers to translate business requirements into reliable pipeline and data model designs
Contribute to team knowledge sharing and drive the advancement of new data engineering capabilities
Mentor team members to build and enhance their data engineering skills and professional growth
Assist management in product definition, including estimating, planning, and scoping work to meet objectives
Also responsible for other duties/projects as assigned by business management as needed
Requirements
Bachelor's Degree plus 5 years of related work experience OR Advanced degree with 3 years of related experience
Acceptable areas of study include Computer Engineering, Computer Science, a related subject area
4-7 years Developing cloud solutions using data series
experience with cloud platforms (Amazon Web Services, Azure, or Google Cloud)
4-7 years Hands-on development using and migrating data to cloud platforms
Proven track record in SQL, NoSQL, and/or relational database design and development
4-7 years Advanced knowledge and experience in building sophisticated data pipelines with Python, Experience in languages such as SQL, DAX Python, Java, Scala, and/or Go
Hands-on experience building, operating, and solving production data pipelines in cloud, hybrid, or data lakehouse environments
Experience developing scalable data models, transformation logic, and curated datasets using SQL, Python, Databricks, or comparable data engineering technologies
Experience implementing data quality, reconciliation, monitoring, or validation controls for business-critical data pipelines
Experience using version control, peer review, release controls, or CI/CD practices in a production engineering environment
Ability to collaborate with architects, product managers, business partners, and engineering teams to translate requirements into reliable data solutions
Cloud Computing
Collaboration
Data Analysis
Data Engineering
Data Lake
Data Management
Data Modeling
Data Warehousing (DW)
Databricks DBRX
DBT (Data Build Tool) Framework
At least 18 years of age
Legally authorized to work in the United States
Nice to have
8+ years of data engineering experience
5+ years of experience in cloud-based data lakehouse environments
Proficiency in building and using AI stack
Experience with Azure Data Lake Storage Gen2, Azure Data Factory, Databricks Workflows, Delta Lake, Unity Catalog, or Delta Live Tables
Experience with dbt Core, including model layering, testing, documentation, and lineage
Experience migrating legacy ETL, reporting, or analytics workloads from tools such as Alteryx, SAS, SSIS, Informatica, IBM TM1, SSAS, or on-premises SQL Server to modern cloud data platforms
Experience with Finance domain data, including ERP, planning and forecasting, revenue accounting, tax, treasury, securitization, or regulatory reporting datasets
Experience supporting SOX, ITGC, audit-ready lineage, SEC reporting, TISS-310 data classification standards, T-Mobile QSR, or other compliance-sensitive data environments
Experience with GitLab CI/CD, Azure DevOps, automated regression testing, or controlled promotion paths across environments
Familiarity with Oracle ERP, Oracle EBPCS/BPM, Snowflake, Jira, Confluence, Microsoft Teams, or related enterprise data and collaboration tools
Databricks, Azure, AWS, Google Cloud, or related cloud/data certification
Cloud platform certification(s) i.e. AWS Certified Solutions Architect, AWS Certified Cloud Practitioner or Microsoft Certified Solutions Associate (MCSA)