Analytics Data Engineer

US Mobile•New York, NY

2d•$140,000 - $190,000

About The Position

US Mobile is building the future of wireless communication with the goal of one unified network open to any person and any device, worldwide. They are empowering customers by offering all three major networks on one phone and one plan, plus home internet from Starlink, with no lock-in or commitments. They provide custom-fit plans at every price point and 24/7 customer support with real people. The company utilizes real-time feedback from Reddit, surveys, and customer support to inform product roadmaps. Consumer Reports has named them the top-rated mobile carrier two years in a row. US Mobile is building innovative systems that scale with a network-agnostic tech stack and agile, cross-functional teams built on trust and mutual respect. This work is for individuals who work fast, flexibly, and collaboratively without compromising standards. We're looking for an Analytics Data Engineer to help build, scale, and maintain the data foundation that powers decision-making across the company. You'll work closely with stakeholders across product, analytics, engineering, and operations to understand business needs, design reliable data models, and deliver high-quality reporting and analytics 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.
Hands-on experience designing and maintaining reliable data pipelines using modern tools such as dbt, Dagster, Redshift, or equivalent.
Proficiency in a scripting language; Python preferred.
Demonstrated use of AI in your day-to-day workflow — LLM-assisted coding or prompt-driven data exploration.
Experience building dashboards and analytical tools in Hex, Tableau, or comparable BI/reporting platforms.
Strong understanding of data modeling, metric design, and analytics engineering best practices.
Strong communication skills, with a bias toward clear documentation, reliable systems, and measurable business impact.

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.
Transform raw data into trusted, canonical datasets for reporting, analysis, experimentation, and decision-making.
Build high-performance dashboards and analytical tools in Hex, Tableau, or similar platforms.
Use AI tooling to accelerate pipeline development, SQL/dbt work, and documentation.
Partner with data scientists, analysts, and engineers to bridge business needs and data infrastructure.