US Mobile
Analytics Data Engineer
New York City
Sponsorship not specified$140k-$190kDetected 27 days ago
PythonSQLRedshiftdbtData EngineeringData VisualizationLLMsA/B TestingCustomer SupportCommunication
About the role
- The goal: one unified network open to any person and any device, worldwide.
Responsibilities
- Partner with stakeholders to understand data needs and translate business questions into technical requirements for data models, pipelines, dashboards, and self-service tools.
- Design, build, document, and maintain reliable data pipelines using dbt, Dagster, Redshift, and related tools.
- Build high-performance dashboards and analytical tools in Hex, Tableau, or similar platforms.
- Partner with data scientists, analysts, and engineers to bridge business needs and data infrastructure.
Requirements
- 5+ years of experience in analytics engineering, data engineering, or business intelligence, ideally in a fast-paced, high-growth environment.
- Strong SQL skills, including experience with large datasets, complex transformations, and performance optimization.
- Strong understanding of data modeling, metric design, and analytics engineering best practices.
Nice to have
- one unified network open to any person and any device, worldwide.
- Connection without walls.
- All three major networks on one phone and one plan, plus home internet from Starlink.
- Custom fit plans at every price point.
- A network agnostic tech stack.
- Agile, cross-functional teams built on trust and mutual respect.
- This work isn't for everyone.
- If you work fast, flexibly, and collaboratively - without compromising standards - we want to hear from you.
Compensation
- $140k-$190k
Benefits
- Health care coverage through Aetna (Medical, Dental, Vision)
- Flexible paid time off
- Gym reimbursement (up to $100 a month)
- Commuter reimbursement (MTA, Metro North, etc..)
Apply directly at US Mobile →Create a free account for alerts like thisView US Mobile immigration profile
This listing is sourced directly from US Mobile's careers page and normalized into a canonical job model.