Data Engineer

🕒 May 5

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SandboxAQ

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2021

🤖 Artificial Intelligence

🔒 Cybersecurity

💊 Pharmaceuticals

Artificial Intelligence • Cybersecurity • Pharmaceuticals

SandboxAQ is a company developing quantitative AI and quantum-inspired technologies to solve real-world problems across drug discovery, materials science, cybersecurity, and navigation. They build large quantitative models (LQMs) grounded in physics and chemistry to predict molecular properties, accelerate therapeutic design, and simulate complex chemical and catalytic processes, while also applying their expertise in AI and post-quantum cryptography to enhance digital security and navigation in GPS-denied environments.

📋 Description

• 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

🎯 Requirements

• 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)

🏖️ Benefits

• 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