Sr Engineer, Data - Finance Domain

T-Mobile•Frisco, TX

20h

About The Position

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. We are a team that encourages innovation and advocates an agile and open approach, truly working and playing in the Un-carrier way!

Requirements

Bachelor's Degree plus 5 years of related work experience OR Advanced degree with 3 years of related experience (Required)
Acceptable areas of study include Computer Engineering, Computer Science, a related subject area (Required)
4-7 years Developing cloud solutions using data series; experience with cloud platforms (Amazon Web Services, Azure, or Google Cloud) (Required)
4-7 years Hands-on development using and migrating data to cloud platforms (Required)
Proven track record in SQL, NoSQL, and/or relational database design and development (Required)
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 (Required)
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 (Required)
Collaboration (Required)
Data Analysis (Required)
Data Engineering (Required)
Data Lake (Required)
Data Management (Required)
Data Modeling (Required)
Data Warehousing (DW) (Required)
Databricks DBRX (Required)
DBT (Data Build Tool) Framework (Required)
At least 18 years of age
Legally authorized to work in the United States

Nice To Haves

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) (Preferred)
DBT Analytics or Architect certification (Preferred)

Responsibilities

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.