Data Operations Analyst

Brain Corp is a San Diego, California, USA-based AI company creating transformative core technology for the robotics industry. Our purpose is to create autonomous technology that helps the real world work better. Brain's robotic and AI solutions help retailers ensure that the right product is on the right shelf at the right price, in a clean environment. Through the BrainOS® Robotics Platform, which powers the largest global fleet of the Autonomous Mobile Robots (AMRs) in operation in commercial public spaces, Brain Corp delivers insightful and efficient automated solutions in both commercial floor cleaning and inventory management, empowering organizations and their employees to achieve more. Brain Corp currently powers more than 30,000 AMRs, representing the largest fleet of its kind in the world. Brain Corp is funded by the SoftBank Vision Fund, Clearbridge, and Qualcomm Ventures.

Named a top workplace by the San Diego Union Tribune and USA today in 2025, we make life-changing impacts through innovation, helping workers globally unlock their abilities in orchestration with intelligent machines.

Position Overview

The Senior Data Operations Analyst owns the hands-on work behind our operational data — turning raw, multi-stage robotic and fleet data into trustworthy, well-defined, and monitored metrics the organization can rely on. Working directly in BigQuery, this person defines the metrics decisions are built on, validates their accuracy, and builds the monitoring and tooling that keep operational data trustworthy at scale. The ideal candidate is equally a hands-on data investigator and a rigorous systems thinker, comfortable working through messy, ambiguous data to pin down what metrics actually mean — and making sure that knowledge is documented and accessible across the organization.

Essential Job Functions

Data Ownership & Definition

• Define and document key operational and quality metrics hands-on with engineering, keeping them current as pipelines and features evolve.
• Build and maintain data lineage and a documented view/metric layer so critical knowledge is documented and shared across the organization.
• Validate data quality and proactively audit existing metrics for accuracy and relevance as the platform evolves — catching discrepancies before they drive decisions and flagging definitions that have drifted or pipelines that no longer reflect ground truth. 
• Partner day-to-day with Data Engineering, drafting well-formed requests for durable, managed data sources where long-term gaps exist.

Fleet & Performance Analysis

• Work directly with operational and robotic data in BigQuery (SQL) to investigate fleet trends, performance, and quality metrics — owning the analysis, not just reviewing outputs.