Software Engineer - Infrastructure, Data Platform

Baton•San Francisco, CA

14d•$162,000 - $216,000•Hybrid

About The Position

Baton is Ryder ’s in-house product development group focused on harnessing emerging technologies to redefine transportation and logistics. With $10B in freight under management, our technology reaches every part of the U.S. economy. We design and ship category-defining software that enables Ryder and its 50,000+ customers—including some of the world’s most well-known brands—to plan and execute freight intelligently, efficiently, and cost-effectively. Our work includes everything from customer-facing software to the data platform that will power the next era of innovation at Ryder. Baton’s mission : enable supply chain on autopilot. Ryder acquired Baton in 2022 to power its next wave of digital products. We operate at startup speed, with Fortune 500 reach. If you have a passion for solving complex problems and creating impact for the engine of the American economy, you’ll love it here. Role: Software Engineer - Infrastructure Department: Data Platform Location Hayes Valley, San Francisco, CA Basic Job Details Job Type: Full Time Work Model: Hybrid Remote Days: Monday & Friday Office Days: Tuesday, Wednesday, Thursday

Requirements

Data Systems Expertise Demonstrates experience with real-time data systems
Python Proficiency Advanced proficiency coding in python

Nice To Haves

Bachelor’s or Master’s Degree in Computer Science, Engineering, or a related technical field.
Demonstrated interest or experience in the freight and logistics industry, with a keen understanding of its data-driven challenges and opportunities.
Strong foundation in data analytics and big data technologies, with the ability to leverage these skills to drive insights and operational efficiencies.

Responsibilities

Design and maintain a robust data streaming and messaging architecture.
Ownership of our near real-time data streaming platform.
Act as a technical liaison to ensure alignment of data strategies with business goals.
Take charge of the entire data landscape, encompassing near real-time data ingestion, sophisticated processing, and efficient data consumption strategies.
Collaborate closely with data stakeholders to simplify and solve complex data challenges, transforming them into scalable and maintainable technical solutions.
Everything from developing and maintaining data pipelines, enhancing data models, to refining API layers.