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Remote opportunity atServe Robotics

Data Analyst, Supply Chain

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Published
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5 Oct 2026Apply before
Opportunity details

About this role.

AI Summary

Serve Robotics seeks a senior Data Analyst to support supply-chain data management, operational analytics, and integrations with logistics partners. The role combines analytics with hands-on data engineering, including API workflows, automated pipelines, data models, dashboards, alerts, and data-quality controls. The analyst will partner with Supply Chain, Operations, Finance, and Engineering teams to define KPIs, investigate operational issues, and improve logistics performance. Strong SQL, Python or JavaScript, cloud data experience, and modern AI-assisted development capabilities are central to the position.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a technically broad, senior-level role requiring both analytical and engineering execution across production data workflows. Success also depends on independently prioritizing cross-functional requests and translating complex supply-chain data into operational recommendations.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianHighly competitive
$115,000
US market range$90k–$140k
AI insightNo salary was disclosed, so these figures are estimated USD yearly compensation for the US market for a mid-to-senior Data Analyst with supply-chain analytics, SQL, API integration, and data-pipeline responsibilities. The estimated market range is $90,000-$140,000, with an estimated midpoint of $115,000; actual pay may differ by country, employer, and total compensation structure.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe a data pipeline you built that integrated an external partner API with internal operational systems.

I would explain the source and target systems, the API authentication and ingestion approach, transformation logic, scheduling, retries, and monitoring. I would also quantify the result, such as reduced manual work, faster reporting availability, or improved data completeness.

How would you investigate a sudden increase in average shipping time across delivery operations?

I would first validate the metric and check for data freshness or definition changes. Then I would segment performance by geography, carrier or partner, fulfillment node, order type, time period, and inventory availability to isolate contributors, validate findings with operations teams, and recommend targeted actions.

What practices do you use to ensure data quality in automated reporting pipelines?

I use schema validation, freshness checks, volume and null-rate thresholds, reconciliation against source systems, idempotent loads, and alerting for failures or anomalous values. I also document metric definitions and assign clear ownership for remediation.

How have you communicated a complex analytical finding to nontechnical stakeholders?

I focus on the operational question, state the recommendation clearly, and use a concise dashboard or visual to show the magnitude and drivers. I explain assumptions and limitations in plain language, then align on the decision, owner, and measurement plan.

How would you use AI-assisted development tools responsibly when building production data workflows?

I would use them to accelerate drafting code, tests, documentation, and troubleshooting, while retaining human review of business logic, security, and data handling. I would validate generated code through version control, automated testing, peer review, and controlled deployment practices.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.

At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses.

The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity.

Who We Are

We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully.

The Data Analyst drives data management workflows and performs data analysis. This role connects our internal systems with various logistics partners, builds data pipelines, calculates metrics, and creates dashboards & alerts to highlight operational bottlenecks. This position leverages AI tools and frameworks to build faster, create scalable API connections, and manage the data architecture that keeps Serve’s supply chain running.

JOB DUTIES

  • Develop and manage API-based workflows to ensure seamless communication between our core business platforms and external logistics partners.

  • Design, build, and maintain automated pipelines to retrieve and route data across various internal and external databases and tools.

  • Perform hands-on data analysis (e.g., tracking average shipping times, inventory flows, etc.) and build automated spreadsheets and/or dashboards for stakeholders.

  • Implement error handling, alerting, etc. to ensure data workflows run consistently and accurately.

  • Partner with Supply Chain, Operations, Finance, and Engineering stakeholders to define KPIs, reporting requirements, and data quality standards across supply chain processes.

  • Conduct root cause analysis on operational issues and recommend process improvements that reduce cost, improve inventory accuracy, and increase logistics performance.

  • Create and maintain technical documentation for data models, integrations, workflows, and reporting systems to support long term scalability and knowledge sharing.

EXPERIENCE, QUALIFICATIONS, & SKILLS

Required Experience, Qualifications, and Skills

  • 5 years of hands-on experience in data analysis, data engineering, workflow automation, or similar technical roles.

  • Strong general knowledge of data structures and data management, both for big data using cloud databases and small data using excel & google sheets.

  • Advanced knowledge of SQL and at least one other programming language (ideally Python or Javascript).

  • Proficiency in modern AI-driven development including AI coding tools (e.g., Claude Code, GitHub Copilot, Codex), Model Context Protocol (MCP), configuration of Skills, sub-agents, etc. and familiarity with popular AI orchestration frameworks such as Langgraph, OpenAI API/SDK, Gemini API/SDK, etc.

  • Experience designing data models and transforming data from multiple operational systems into clean, reliable datasets for reporting and analysis.

  • Strong analytical and problem solving skills, with the ability to translate complex data into actionable business recommendations for nontechnical stakeholders.

  • Demonstrated ability to independently manage multiple projects, prioritize competing requests, and deliver high quality work in a fast paced environment.

Preferred Experience, Qualifications, and Skills

  • Google Cloud Platform (GCP): Specifically Cloud Run, BigQuery, and other supporting services.

  • Google Workspace / G Suite: Specifically Google Sheets and Google Apps Script.

  • NetSuite: Specifically working with the NetSuite API.

  • Supply Chain Tech Experience: Applying these data and technology skills specifically within supply chain, logistics, or e-commerce operations.

  • Experience working with third party logistics (3PL), fulfillment, inventory management, or transportation management systems.

  • Familiarity with data observability, monitoring, and data quality frameworks in production environments.

Apply now >

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