Data Engineer

SandboxAQ

Data Engineer building and optimizing data pipelines for quantum navigation solutions at SandboxAQ. Collaborating with a diverse team to enhance data infrastructure and support advanced models.

Posted 5/5/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $140,800 - $264,000 per yearWebsite

Tech Stack

Tools & technologies

AWSCloudDockerPythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonSQLdata pipeline developmentdata modelingdata qualityanomaly detectionMLOpsCI/CDcontainerizationdata warehousing

Soft Skills

cross-functional collaborationrequirement translationdocumentationproblem-solvingcommunication