What we need

We are seeking a self-driven, analytical, and strategic Manager of Automation Performance & Analytics to build and lead a high-impact team that unlocks insights from our rapidly scaling warehouse automation systems.

This role is foundational — not only will you shape the analytics function from the ground up, but you’ll also partner closely with engineering, operations, software, and leadership to uncover patterns, drive improvements, and scale performance monitoring from one site to many more.

The ideal candidate thrives in ambiguity, enjoys working across data and engineering boundaries, and brings a strong command of both technical depth and business communication. You'll use advanced analytics and AI/ML where appropriate to move beyond surface-level metrics and reveal hidden inefficiencies that matter.

What we do

Breakpack is an innovative new adapter cell, designed as a high-value add-on to the Symbotic System. The Breakpack adapter cell operates alongside or inside the Symbotic core structure, spans across all levels and integrates several robotic systems. Breakpack allows the Symbotic system to sort and deliver individual items rather than just full cases. When integrated with our full-case system, it provides our customers enormous flexibility for how they deliver items to their hundreds of stores. This is a critical and strategic product for Symbotic.

What you’ll do

• Operate autonomously to analyze and model performance across robotic and non-robotic automation systems (e.g., conveyors, sorters, robotic arms, WES/WCS software).

• Identify gaps in data availability and partner with software teams to implement new telemetry and data contracts in a microservice-based, event-driven architecture.

• Build and lead a growing analytics team focused on performance, insights, and system optimization.

• Dive deep into data — beyond surface-level metrics — to expose inefficiencies, bottlenecks, and root causes.

• Translate complex data patterns into strategic recommendations and communicate them effectively to executive and operational leadership.

• Create dashboards, visualizations, and reports that clearly articulate trends, patterns, and performance KPIs.

• Collaborate with engineering, software, and operations teams to influence system behavior and automation rules based on analytical findings.

• Establish long-term frameworks for data governance, monitoring, and performance measurement across multiple automation-enabled facilities.

• Apply advanced analytics or AI/ML techniques where appropriate to identify leading indicators of performance degradation, bottlenecks, or systemic inefficiencies.

• Design and implement the foundational data model and KPIs for automation system performance.

• Launch insightful dashboards and analysis processes across multiple warehouses.

• Establish a regular communication cadence with leadership to turn insights into action.

• Hire and develop a capable analytics team to support the growing automation network.

What you’ll need

• Bachelor’s degree or higher in Data Science, Engineering, Applied Mathematics, or a related technical field.

• Minimum of 8 years of experience in data analytics, performance engineering, or automation systems analysis — ideally in logistics, manufacturing, or warehousing.

• Proficiency in data management and analysis tools, including SQL, Snowflake, MATLAB, or Python.

• Experience with data visualization platforms such as Power BI, Tableau, or Grafana.

• Solid understanding of microservices and event-driven architectures (e.g., Kafka, RabbitMQ), and how to extract actionable insights from distributed systems.

• Proven ability to operate independently in ambiguous, data-scarce environments and build analytical structure from the ground up.

• Strong communication skills with a track record of presenting complex insights clearly to senior leadership.

• Experience building or leading technical or analytical teams.

• Familiarity with warehouse control systems (WCS), warehouse execution systems (WES), or robotic orchestration platforms.

• Exposure to structured logging or telemetry pipelines (e.g., ELK, Loki, Prometheus).

• Experience using AI/ML for predictive analytics, anomaly detection, or system modeling (e.g., scikit-learn, TensorFlow, Azure ML).

• Understanding of queuing theory, simulation modeling, or performance benchmarking in automated environments.

Our environment

• Up to 10% of travel may be required. Employees must have a valid driver’s license and the ability to drive and/or fly to client and other customer locations.

• The employee is responsible for owning a credit card and managing expenses personally to be reimbursed bi-weekly.