Software Engineer, Analytics Platform

Zoox is transforming transportation with a mission to build autonomous robotaxis from the ground up—delivering a safer, cleaner, more reliable, and enjoyable future for all.

The Analytics Platform, established in late 2024 as part of the Data Infrastructure team, has been redefining the data analysis and metrics infrastructure to enable machine learning, data science, engineering, and safety analysis using modern big data technologies. The team has been developing Zoox's schedule-based and event-based data processing platforms, using technologies such as Spark (AWS EMR) and DuckDB, enabling multiple teams to transform several PBs of structured data into more efficient, queryable formats. The team is also reshaping Zoox's data discovery system, making it easier for engineers, analysts, and data scientists across the company to find, understand, and trust the data they depend on.

By joining this team, you'll collaborate with a world-class group of software engineers to tackle complex challenges at the scale of hundreds of petabytes of data, pushing the boundaries of what's possible in autonomous transportation.

In this role, you will:

• Develop features for the schedule-based processing framework built on top of Airflow, AWS EMR, and DuckDB.
• Improve the stability, performance, and scalability of our data ingestion and processing platforms as we scale our geofence and robotaxi deployment.
• Collaborate with cross-functional teams, such as software engineers, data scientists, data engineers, and TPMs, to gather requirements, design robust architectures, and implement effective solutions.
• Partner with Staff and Senior engineers inside and outside the organization to translate user pain points into concrete technical solutions and roadmap items.
• Enhance system observability by building monitoring and alerting tools to track performance and measure success.

Qualifications

• Bachelor's or Master's in Computer Science or related fields with 4+ years of industry experience in software engineering
• Strong background in Python for large-scale data processing
• Familiarity with large-scale data processing systems like Spark, Trino, and DuckDB
• Experience in using cloud services, such as AWS, GCP, or Azure
• Strong experience in troubleshooting data pipeline failures and optimizing pipeline performance and cost efficiency