Sr Engineer, Data

T-Mobile•Bothell, WA

9h•Onsite

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. This role supports the Customer Information domain, which enables trusted customer data across digital, retail, care, partner, individual, and business channels. The Sr. Engineer, Data helps design, build, and operate reliable, scalable data pipelines, reusable data models, standardized schemas, and approved self-service data products that support analytics, marketing, customer experience, fraud detection, and AI/ML use cases. The role includes mentoring team members to enhance their data engineering skills and capabilities. Success is measured by the effectiveness, reliability, quality, and scalability 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. We are a team that encourages innovation and advocate 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)
4-7 years Experience in SQL, NoSQL, and/or relational database design and development (Required)
4-7 years Advanced knowledge and experience in building complex data pipelines with Python, Experience in languages such as SQL, DAX Python, Java, Scala, and/or Go (Required)
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)
At least 18 years of age
Legally authorized to work in the United States

Nice To Haves

4+ years of experience delivering and operating large-scale Oracle relational database platforms, including performance optimization and database change management on RDBMS and/or NoSQL platforms.
5+ years of hands-on experience designing and operating large-scale data pipelines and ETL/ELT workflows in cloud-native environments, such as Databricks, Snowflake, Apache Spark, Kafka, or similar technologies.
Experience building and maintaining data models and schemas, such as dimensional models, Data Vault, or one big table models, that serve multiple downstream consumers including BI, marketing, analytics, and AI/ML teams.
Experience with customer data, Customer 360, customer identity, segmentation, eligibility, fraud detection, or customer experience data products.
Proficiency with PL/SQL and at least one scripting or data engineering language such as Python or Scala.
Experience with pipeline orchestration, transformation, and automated data quality practices using tools such as Airflow, dbt, or similar technologies.
Experience with data cataloging, metadata management, governance workflows, and self-service tooling such as Collibra or similar platforms.
Experience using database change management tools such as Liquibase or similar technologies.
Experience developing and managing cloud data solutions using AWS services such as RDS, Redshift, Glue, or comparable cloud-native data services.
Cloud platform certification(s) i.e. AWS Certified Solutions Architect, AWS Certified Cloud Practitioner or Microsoft Certified Solutions Associate (MCSA) (Preferred)

Responsibilities

Develop data engineering solutions that enable data pipelines, visualization, and analytical tools to support business requirements.
Design and develop data architectures across on-premise, cloud, and hybrid platforms to ensure scalable data infrastructure.
Architect, build, and maintain reliable data pipelines that ingest, transform, and deliver data from diverse customer and business data sources into enterprise data lake, warehouse, and analytics platforms.
Design reusable data models and standardized schemas, including Customer 360 and customer behavioral data structures, to support consistent metric definitions and trusted downstream use by analytics, marketing, and AI/ML teams.
Perform data wrangling, exploration, and discovery of heterogeneous data to generate new business insights.
Embed data quality practices into pipeline development, including automated checks for completeness, accuracy, timeliness, and governance alignment.
Build and curate self-service data products, metadata, and documentation that improve data discoverability and enable approved users to access trusted datasets efficiently.
Partner with analytics, marketing, AI/ML, and business stakeholders to understand data needs, define data contracts, and support scalable customer data use cases.
Contribute to team knowledge sharing and drive the advancement of new data engineering capabilities.
Mentor team members to build and enhance their data engineering skillsets and professional growth.
Assist management in project definition, including estimating, planning, and scoping work to meet objectives.
Also responsible for other duties/projects as assigned by business management as needed.