OnTrac is the solution of choice for last-mile e-commerce deliveries that helps retailers and shippers build a competitive advantage through faster delivery times, lower costs, coast-to-coast coverage, and reliable on-time performance.

OnTrac’s footprint stretches across the United States to reach approximately 70% of the population in 35 states and Washington, D.C. and enhance retailers’ ability to meet growing demand in the consumer e-commerce delivery market. OnTrac has evolved into a critical part of the e-commerce infrastructure and is trusted by leading retailers and shippers that desire reduced transit times and increased flexibility within their supply chains.

Employment Logistics:

OnTrac is looking for an experienced Data Analytics & BI Analyst to support first- and middle-mile transportation operations. This individual will play a key role in analyzing performance metrics, supporting network planning and design, contributing to strategic initiatives, and identifying cost and operational efficiency opportunities – all in a fast-paced logistics environment.

Your Mission in Motion:

Build and manage linehaul scheduling tools and processes, including forecasting truck needs and tracking lane-level performance metrics.

Lead variance analyses and deep dives into truck utilization, cancellations, and adjusted loads to uncover trends and drive cost efficiency.

Support network planning by developing data-driven business cases and partnering with engineering and operations to implement changes.

Present insights and strategic recommendations to stakeholders across all levels to enable informed decision-making.

Own strategic initiatives such as tooling strategy, peak surge planning, and scenario modeling to optimize the linehaul network.

The Must-Haves:

Bachelor’s degree in a relevant field

7–10 years of experience in transportation, logistics, or supply chain analytics

Advanced Excel proficiency

Strong SQL skills and experience with data visualization tools (Power BI, Tableau)

Willingness to travel occasionally (<10%)