Senior Staff Engineer, Data Infrastructure

<div class="content-intro"><p><span style="font-weight: 400;">Archer is an aerospace company based in San Jose, California building an all-electric vertical takeoff and landing aircraft with a mission to advance the benefits of sustainable air mobility. We are designing, manufacturing, and operating an all-electric aircraft that can carry four passengers while producing minimal noise.</span></p> <p><span style="font-weight: 400;">Our sights are set high and our problems are hard, and we believe that diversity in the workplace is what makes us smarter, drives better insights, and will ultimately lift us all to success. We are dedicated to cultivating an equitable and inclusive environment that embraces our differences, and supports and celebrates all of our team members.</span></p></div><h2><strong>Senior Staff Engineer, Data Infrastructure (Hybrid-San Jose, CA)</strong></h2> <p> </p> <h3><strong>The Mission</strong></h3> <p>We are looking for a heavy-hitter to build the "Data Backbone" of our company. You will be responsible for the architecture, scaling, and reliability of the infrastructure that powers our Data Engineering and ML teams. Your goal is to provide a seamless, self-service environment where data scientists can go from a <strong>JupyterHub</strong> notebook to a massive <strong>Ray </strong>cluster or <strong>Trino</strong> query without worrying about the underlying hardware.</p> <h3><strong>Responsibilities</strong></h3> <ul> <li><strong>Data Plane Ownership:</strong> Architect and manage the lifecycle of high-throughput data tools including <strong>Trino</strong>, <strong>Ray</strong>, and <strong>JupyterHub</strong> on Kubernetes.</li> <li><strong>GitOps Automation:</strong> Drive a "zero-manual-touch" philosophy using <strong>ArgoCD</strong> and <strong>Terraform</strong> to manage complex, stateful data environments.</li> <li><strong>Observability at Scale:</strong> Build high-cardinality monitoring systems using <strong>VictoriaMetrics</strong> and <strong>Vector</strong> to track pipeline health, data ingestion rates, and system performance.</li> <li><strong>ML Lifecycle Support:</strong> Maintain and optimize <strong>MLflow</strong> for model tracking, ensuring it integrates deeply with our compute and storage layers.</li> <li><strong>Engineering Sovereignty:</strong> As a self-starter, you will identify performance bottlenecks in data processing and proactively implement infrastructure-level optimizations.</li> <li><strong>Reliability:</strong> Participate in on-call rotations for the data stack, treating "data downtime" with the same urgency as a site outage.</li> </ul> <h3><strong>The Technical Toolkit</strong></h3> <table> <tbody> <tr> <td> <p>Focus Area</p> </td> <td> <p>Technologies</p> </td> </tr> <tr> <td> <p><strong>Orchestration</strong></p> </td> <td> <p><strong>Kubernetes Expert</strong> (Scheduling, Affinity, Local NVMe, Resource Quotas).</p> </td> </tr> <tr> <td> <p><strong>Data Compute</strong></p> </td> <td> <p>Deep experience with <strong>Trino</strong> (Presto) and <strong>Ray</strong> (Head/Worker patterns).</p> </td> </tr> <tr> <td> <p><strong>Stream Logs</strong></p> </td> <td> <p>High-performance routing via <strong>Vector</strong> and monitoring with <strong>VictoriaMetrics</strong>.</p> </td> </tr> <tr> <td> <p><strong>AI/ML Tooling</strong></p> </td> <td> <p><strong>MLflow</strong> and <strong>JupyterHub</strong> (Zero-to-JupyterHub on K8s).</p> </td> </tr> <tr> <td> <p><strong>Code Deploy</strong></p> </td> <td> <p><strong>Terraform</strong> (Advanced modules) and <strong>ArgoCD</strong> (ApplicationSets/Blue-Green).</p> </td> </tr> </tbody> </table> <p> </p> <h3><strong>Qualifications: </strong></h3> <ul> <li><strong>The "Data-Aware" Engineer:</strong> You understand that scaling a database or a Ray cluster is different from scaling a stateless API. You know how to handle persistent volumes and data gravity.</li> <li><strong>Senior Leadership:</strong> You’ve spent time in the t ... (truncated, view full listing at source)