Senior Service Analyst

Job description

**San Francisco Bay Area (Hybrid — Burlingame office 2-3x/week required)** *Must currently reside locally; this is not a remote-eligible role.* --- Simbe Robotics is a leading retail robotics company providing in-store intelligence solutions that help retailers optimize operations, improve shelf execution, and deliver valuable data insights. Our autonomous robots and multi-modal data collection systems are transforming how retailers manage inventory and make data-driven decisions. ### Position Overview Simbe's Data Science & Analytics team is looking for a Senior Service Analyst to own end-to-end visibility into how well we're delivering on our client commitments. You'll sit at the intersection of client success, engineering, and operations. You will be building the processes, owning the investigations, and strengthening the cross-functional partnerships that keep our service quality measurable, accountable, and continuously improving. This is a delivery-driven role, not analytics for its own sake. Success is measured by whether our clients received accurate, timely data, not by the elegance of the analysis. This role demands someone who is analytically rigorous and operationally disciplined. You'll be tracking dozens of moving pieces across clients, SLAs, and system behaviors simultaneously. The role requires both the attention-to-detail to catch what others miss and the organizational instincts to keep it structured and actionable. You'll be personally building, owning, and operating the SOPs day to day, not directing a team. ### Responsibilities **Service Monitoring** - Independently define and evolve what "healthy service" looks like across the clients portfolio, partnering with the account team on contracted requirements, accuracy SLAs, and prioritization without needing that framework handed to you - Own tracking and reporting of account and fleet level performance across deliverables, accuracy, and timeliness end-to-end: with the rigor to detect subtle regressions and the discipline to report them consistently, and the judgment to decide what's worth escalating versus monitoring **Analysis & QA** - Lead detailed QA and root cause analysis to identify service gaps and systemic patterns, going beyond surface metrics to understand what's actually happening and why, setting the bar for investigation quality rather than following an existing playbook - Document findings with precision; maintain a clear audit trail of issues, resolutions, and follow-ups - Drive issues to resolution directly with the engineering team, exercising independent judgment on prioritization and escalation; own the outcome, not just the handoff, and verify that quality actually improved **Process & Documentation** - Own and continuously evolve the service observability handbook and standard operating procedures — proactively identifying where process is missing or breaking down, not just maintaining what exists - Bring structure to recurring workflows so that nothing falls through the cracks across teams, and identify opportunities to scale or automate as the client base grows **Cross-functional Collaboration** - Work with the data-labeling team to forecast resource requirements given contracted accuracy SLAs - Act as a trusted, senior voice with the Computer Vision, Solutions Engineering, Service Operations, and Client Success teams — shaping how the system gets configured to maximize service quality, not just executing on requests - Host office hours and mentor others on service observability tooling and practices, driving adoption and raising the bar across the broader organization **Tooling & Dashboards** - Build and maintain dedicated dashboards and tooling (Looker, BigQuery, etc.) to monitor service health at the client and fleet level, with full ownership over architecture and design decisions - Maintain a high standard of data accuracy and dashboard reliability. You are the source of truth others depend on, with the autonomy to decide how that trust gets earned and maintained ### Qualifications - 6+ years of full-time, post-degree professional experience in a data analyst, operations analyst, QA analyst, or similar role with a strong analytical and operational bent (internships, research assistantships, and time enrolled in a degree program do not count toward this minimum) - Experience with SLA/SLO tracking or service-level reporting in a SaaS or tech operations context - Strong SQL and data querying skills. Comfortable working in BigQuery or equivalent cloud warehouse - Experience building and maintaining dashboards in BI tools (Looker preferred) - Exceptionally organized, able to manage multiple workstreams, track open issues across teams, and keep documentation current without prompting - High attention to detail: you notice when numbers don't add up, when a trend breaks pattern, or when a process step was skipped - Clear communicator across technical and non-technical audiences; able to translate data findings into crisp operational recommendations ### Preferred Qualifications - Familiarity with dbt or similar data transformation tooling - Exposure to forecasting or capacity planning workflows - Experience mentoring or setting standards for junior analysts