Lead Data Engineer, Analytics & Insights

Job description

## About Us Agtonomy brings intelligent automation to agriculture, turf, and other demanding industries through Physical AI and software services. By partnering with trusted equipment manufacturers, we deliver factory-fit technology that transforms industrial machines into smart, efficient solutions built for safety and performance. Our team combines expertise in technology, product development, and industry knowledge to address critical challenges like labor shortages, sustainability, and productivity. We're looking for people who want to work in a collaborative, fast-moving environment where their ideas can make a real impact. ## About the Role Our fleet produces a continuous stream of data, and we're looking for a senior IC to own how that data becomes insight: for customers, partners, operations, engineering, and leadership. You'll be the bridge between the raw telemetry coming off our machines and the metrics, dashboards, and answers the rest of the company relies on. This is a hands-on role. You'll design the schemas, model the metrics, and ship the React dashboards yourself. You'll also own accountability for the numbers: every metric that leaves your hands needs to be accurate, trustworthy, and defensible when a customer or executive asks how it was calculated. That standard is the most important thing this role brings to Agtonomy. ### What You'll Do - Own the analytics data layer. Design and operate the ClickHouse-backed warehouse that powers operational, business, and autonomy reliability metrics, partnering with the Platform team that owns ingestion. - Define the metrics that matter. Partner with operations, product, engineering, and finance to turn fuzzy questions into trustworthy metrics: autonomy uptime, intervention rates and classifications, release-over-release reliability, mission success, fleet utilization, customer ROI. - Ship customer-facing data experiences. Build React dashboards inside our web and mobile customer analytics portals, partnering with the Applications & Connected Services team. - Power internal decision-making. Build the Grafana boards, custom React dashboards, and self-serve tooling that let teams answer their own questions and catch problems before customers do. - Be accountable for accuracy. Establish the data quality, validation, lineage, and documentation that make every number defensible. When someone asks where a metric came from, you have an answer. - Share ownership of data contracts with the Platform and Cloud software teams. ### What You'll Bring - 7+ years building production data systems, with meaningful time on both data modeling and analytics delivery. - Shipping experience in React. You've built analytics UIs or dashboards in a production web app and are comfortable owning the surface, not just the SQL behind it. - Deep SQL fluency and strong opinions about modeling time-series and event data. ClickHouse experience is a strong plus; comparable OLAP systems (BigQuery, Snowflake, Druid, Pinot) translate. - Comfort working alongside a platform team that owns ingestion. You have experience collaborating with the teams developing the pipeline to ensure access to high quality, actionable data. - Strong Python and at least conversational comfort in one system language (Go, Rust, TypeScript, C++). - Familiarity with semantic layers (dbt, Cube, MetricFlow, etc). - A track record of turning raw telemetry into metrics non-technical stakeholders actually use and trust. You’ve turned loose high-level ideas into actionable and reliable analytics. - High standards for data quality. You've been the person on the hook when a customer-facing number was wrong, and you've built the systems that keep it from happening again. ### Bonus Points - Experience in robotics, IoT, fleet management, automotive, or any domain where diverse physical devices produce telemetry at scale. - Experience building embedded analytics inside a product (customer-facing dashboards, not just internal BI tools). - Experience shipping data experiences in React Native or other mobile frameworks. - Experience with ML-backed analytics — forecasting, anomaly detection, fleet health scoring, release regression detection.