Senior 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. Partner with operations, product, and engineering as an analytical thought partner — translating business questions into structured analysis and findings into clear, actionable recommendations for both technical and non-technical audiences.  Proactively surface signals worth monitoring without waiting to be directed. Turn short-term analyses into repeatable visibility frameworks and standardized KPIs that support scaling and data-driven decisions. Reporting, Monitoring & Tooling Establish and maintain standards for dashboard and metric design that other team members and stakeholders can follow, ensuring consistency and reusability across the reporting layer.  Design, build, and continuously improve dashboards and monitoring across BI tools (Grafana, Looker, Power BI) or custom tooling. Leverage modern AI tooling to build and ship dashboards, automation, and lightweight internal tools at high velocity. Partner with Data Engineering to maintain reliable pipelines and integrations across core systems. Process & Initiative Support Apply data to evaluate and improve operational workflows, identifying where standardization or automation improves consistency and reliability. Coordinate multiple data initiatives, maintaining timelines and tracking to ensure on-time, measurable delivery. Education and/or Work Experience Requirements Bachelor's degree in Data Science, Statistics, Computer Science, Engineering, or a related field. 4+ years in data operations, analytics engineering, business analytics, or a related technical data role, or equivalent demonstrated ownership of data/analytics work. Required Knowledge, Skills, Abilities and Other Characteristics Strong SQL and hands-on experience in a cloud data warehouse (BigQuery preferred). Proficiency with BI / data visualization tools (Grafana, Looker, Power BI, Tableau). Working proficiency in Python (or equivalent scripting) for data work and automation.  Experience designing dashboards or data-monitoring systems for operational or quality metrics. Comfort with messy, multi-stage data and ambiguous field semantics. Self-directed: identifies what matters without being told, and can hold their own in a definitional conversation with engineering. Strong communication and stakeholder skills across technical and non-technical audiences. Things That Make a Difference Direct experience in Data modeling and documentation practice. Familiarity with version control (Git) and collaborative development practices.  Ability to build fluently with modern AI tooling to multiply output. Experience with operational, telemetry, IoT, or sensor data. Background in robotics, IoT, or support operations analytics. Physical Demands : The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Essential functions may require maintaining the physical condition necessary for sitting, walking or standing for periods of time; operating a computer and keyboard; use of hands to finger