Data Engineer

🕒 vor 2 Monaten

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SandboxAQ

WebsiteLinkedInAlle Stellen

51 - 200 Mitarbeiter

Gegründet 2021

🤖 Künstliche Intelligenz

🔒 Cybersecurity

💊 Pharmazie

Artificial Intelligence • Cybersecurity • Pharmaceuticals

SandboxAQ ist ein Unternehmen, das quantitative KI- und quanteninspirierte Technologien entwickelt, um reale Probleme in den Bereichen Arzneimittelforschung, Materialwissenschaft, Cybersicherheit und Navigation zu lösen. Sie erstellen große quantitative Modelle (LQMs), die auf physikalischen und chemischen Grundlagen beruhen, um molekulare Eigenschaften vorherzusagen, therapeutische Designs zu beschleunigen und komplexe chemische und katalytische Prozesse zu simulieren, während sie auch ihr Fachwissen in KI und Post-Quanten-Kryptografie einsetzen, um digitale Sicherheit und Navigation in GPS-losen Umgebungen zu verbessern.

Beschreibung

• Data Pipeline Development & Maintenance: Work across a mixed-maturity pipeline environment • Data Modeling: Build and optimize data models that serve a diverse set of consumers. You'll make the data accessible and trustworthy, not just available • Simulation Data Integration: Work within the in-house simulation suite to add data-capturing capabilities and ensure simulation outputs feed cleanly into downstream pipelines alongside real-world field data • Data Quality & Observability: Instrument pipelines with quality checks, anomaly detection, and alerting so issues surface early • Cross-Functional Data Support: Translate ambiguous asks into well-defined requirements, repeatable datasets and lightweight Dashboards that the team can use independently going forward • Data Platform Infrastructure Contribution: Improve the features and reliability of our internal data platform over time • Documentation: Own the technical documentation for pipelines, data models, and schemas you touch. In a team this cross-functional, good documentation is a force multiplier

🎯 Anforderungen

• 3+ years of industry experience as a Data Engineer in a startup or fast-moving environment. • Strong proficiency in Python and SQL, with hands-on experience building production-grade data solutions. • Experience designing and maintaining data pipelines and data models/warehouses that process large, structured scientific or engineering datasets. • Hands-on experience building on AWS (e.g., S3, ECS, Lambda, IAM) combined with CI/CD and containerization (e.g., GitHub Actions or CircleCI, Docker) to automate, deploy, and maintain data and ML workloads in the cloud. • Practical MLOps experience: setting up and operating MLOps frameworks (e.g., MLFlow, DVC)

🏖️ Vorteile

• Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions • Retirement savings with company matching • Paid parental leave • Inclusive family-building benefits • Flexible paid time off • Company-wide seasonal breaks • Support for flexible work arrangements that enable sustainable performance • Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs