<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <p><strong>About GeneralMind</strong></p><p>GeneralMind builds AI Systems of Action for complex enterprise workflows. We deploy AI employees that automate messy operational handovers across supply chains - processing orders, validating invoices, coordinating suppliers, and handling exceptions directly on top of enterprise systems like SAP, Oracle, JDE, and Dynamics.</p><p>Our platform combines AI agents, operational context graphs, and real transaction data to achieve Autopilot-grade automation in real operational environments.</p><p>We work closely with leading global companies across manufacturing, retail, and logistics to deploy AI into mission-critical workflows.</p><p><strong>About the Role</strong></p><p>We are looking for a Senior Data Engineer who doesn’t fit cleanly into a single box; and that’s intentional. This role sits at the intersection of two disciplines that are rarely mastered together: deep production database work (PostgreSQL performance, schema design, query optimization) and modern data engineering (ETL pipelines, dbt, KPI infrastructure). Most candidates are strong in one. We would love to find someone who is genuinely strong in both.</p><p>You will be a senior individual contributor working directly with the founders. You will shape how data flows through the platform, how it’s modeled, and how it surfaces as insight. The person in this role won’t be handed a roadmap, they’ll help define it, and build the team around it.</p><p><strong>What You Will Do</strong></p><p><strong>Database Engineering &amp; Performance</strong></p><ul><li>Own PostgreSQL as a production system—not just query it. You understand how it works, not just how to use it.</li><li>Monitor, profile, and improve query performance across services: indexes, query plans, vacuuming, connection pooling, and beyond.</li><li>Detect regressions before they become incidents; build the tooling to catch them early.</li><li>Partner with backend engineers to review schema changes, challenge bad data models, and design for longevity.</li></ul><p><br/></p><p><strong>Data Modeling &amp; Platform Architecture</strong></p><ul><li>Intelligently shape data models that serve both operational and analytical needs—understanding when they should diverge and when they shouldn’t.</li><li>Design systems that are easy to query, easy to maintain, and hard to misuse.</li><li>Think in layers: raw, cleaned, modeled, serving—and own the reasoning behind each boundary.</li><li>Spot structural debt before it compounds; propose migrations that are thoughtful, not just correct.</li></ul><p><br/></p><p><strong>ETL Pipelines &amp; dbt</strong></p><ul><li>Build and maintain robust ETL pipelines that move data reliably from source to serving layer.</li><li>Use dbt to model, test, and document transformations—not as a checkbox, but as engineering discipline.</li><li>Own pipeline observability: know when data is stale, broken, or drifting before downstream consumers do.</li><li>Think carefully about idempotency, failure modes, and recovery—not just the happy path.</li></ul><p><br/></p><p><strong>KPIs, Reporting &amp; Stakeholder Tooling</strong></p><ul><li>Collaborate with product and leadership to define metrics that actually reflect business reality.</li><li>Build dashboards that are actionable—for both technical and non-technical audiences.</li><li>Enable self-serve analytics so teams can answer their own questions without always coming to you.</li><li>Maintain a single source of truth for core KPIs; push back when definitions drift.</li></ul><p><br/></p><p><strong>What We Are Looking For</strong></p><ul><li><strong>Experience: </strong>6+ years in data engineering or a combination of backend and data roles, with demonstrated ownership of production systems.</li><li><strong>PostgreSQL depth: </strong>This is non-negotiable. You can read a query plan, diagnose a performance issue, and fix it at the right layer. You know when the problem is the query, the schema, or the configuration.</li><li><strong>Data modeling judgment: </strong>You think carefully about how data is structured and why. You know the difference between a model that works today and one that scales for two years.</li><li><strong>ETL &amp; dbt fluency: </strong>You’ve built pipelines that run reliably in production, and you’ve used dbt beyond the tutorial. You test your transformations and document your models.</li><li><strong>Dual-track comfort: </strong>You can switch context between a slow PostgreSQL query in production and a dbt model that’s producing wrong numbers—without losing your footing in either.</li><li><strong>Ownership: </strong>You identify what’s broken or missing before you’re asked. You follow problems to their root and don’t leave the system worse than you found it.</li><li><strong>Communication: </strong>You can explain a data model or a pipeline architecture to a product manager without losing precision. Clarity is part of your craft.</li><li><strong>ClickHouse: </strong>A meaningful plus. Experience with ClickHouse for analytical workloads puts you ahead.</li><li><strong>AI/ML data exposure: </strong>A meaningful plus. Experience building data infrastructure for AI or ML use cases—feature stores, embedding pipelines, evaluation datasets—is directly relevant to where we’re headed.</li></ul><p><br/></p><p><strong>Why Join Us</strong></p><p>We’re a small team building something genuinely complex—a platform where data quality and data architecture have direct consequences for how the product works and how the business runs. You won’t be maintaining a warehouse someone else designed. You’ll be making foundational decisions about how data is modeled, moved, and surfaced, with the autonomy to do it right. If you’re the kind of engineer who finds satisfaction in both a perfectly tuned query and a clean dbt DAG, this is your role.</p> </div>