Senior Analytics Engineer (f/m/d)

<h3>About You</h3> <p>You turn messy data into crisp signals that change decisions. You write SQL daily, love making data auditable, and enjoy seeing your work power real product features. You bridge data and product - owning the insight layer, not the plumbing.</p> <h3>About Earlybird</h3> <p>Founded in 1997, Earlybird identifies and backs exceptional early-stage companies on a pan-European basis – supporting them through their growth and development phases and providing financial resources, strategic support, plus access to an international network and capital markets. Through two separate strategies, Earlybird focuses on tech-enabled businesses in fintech/ insurtech, enterprise software, and deep tech (energy, food, and space) while Earlybird Health focuses on improving patient outcomes. Earlybird additionally has a Growth Opportunity Fund for follow-on investments, and a pro-bono impact initiative, Vision Lab. With EUR 2.5 billion under management across fund streams and a history of 9 IPOs and 35 trade sales, Earlybird is among Europe’s most established and active venture capital firms.<br/><br/>More information on<a href="http://www.earlybird.com/"> www.earlybird.com</a>, <a href="https://linkedin.com/company/earlybird-venture-capital">LinkedIn</a>, and <a href="https://www.twitter.com/EarlybirdVC">X</a>.<br/><br/>This role exists because EagleEye needs a dedicated owner of the <span>semantic + scoring layer</span> and <span>make data available to our internal users</span> — true to Earlybird’s roll-up-sleeves ethos and ambition to back Europe’s next tech leaders.</p> <h3>At Earlybird, your projects, responsibilities, and tasks will likely include:</h3> <ol><li><strong>Build</strong> semantic SQL models (Founder, Startup, Funding, Markets) that translate raw data into investor-ready insights.</li><li><strong>Own</strong> scoring and prioritization frameworks for stealth-founder detection; version and deploy via SQLMesh.</li><li><strong>Deliver</strong> reliable <strong>BigQuery→Postgres</strong> batch workflows (no realtime), with clear SLAs, monitoring, and runbooks.</li><li><strong>Enable</strong> investor workflows by surfacing dashboards, signals, and lists that guide outreach and decisions.</li><li><strong>Embed</strong> a shared language across teams—glossary, canonical terms, and entity-resolution rules.</li><li><strong>Harden</strong> quality with SQLMesh tests/validations; keep models auditable, reproducible, and safe to change.</li></ol> <h3>What we hope you bring to the role:</h3> <strong>Core</strong><br/><ul><li>You write SQL daily and ship maintainable models in BigQuery and Postgres.</li><li>You’ve delivered analytical data products (dashboards/signals/scoring) that changed decisions.</li><li>You operate safe batch workflows (design, schedule, monitor, troubleshoot) and explain trade-offs clearly.</li><li>You communicate simply, document well, and collaborate with non-engineers.</li></ul><strong>Nice-to-have</strong><br/><ul><li>Experience with SQLMesh/dbt, backtesting simple scoring models, or light ML.</li><li>Familiarity with venture data or investor workflows.</li></ul><em>If you meet most core requirements, please apply.</em>