What We Do At GoGuardian, we’re helping build a future where all learners are ready and inspired to solve the world’s greatest challenges. Our award-winning system of learning solutions is purpose-built for K-12 and trusted by school leaders to promote effective teaching and equitable engagement while helping empower educators to keep students safe. ; What It’s Like to Work at GoGuardian We are an outcomes-focused learning company with a steadfast focus on improving learning environments, one classroom at a time. Working with us means joining a remote team of diverse, committed, mission-driven employees who are inspired by our vision, dedicated to our customers, and ready to roll up their sleeves. Guardians put their heads together to solve problems, learn together from experiments that fail, and stand together by their work with full accountability. We balance our diligence with an inclusive culture that invites everyone to bring their whole self to work. Join us and learn why “I love the people here” is one of the most frequent comments we hear from Guardians. The Role We’re looking for a Data Engineer II to help design, build, and continuously improve the GoGuardian Analytics and AI/ML ecosystem. This position sits on the Data Engineering team, a group responsible for building and maintaining the core data platform that powers analytics, product insights, and machine learning across the company. You’ll collaborate closely with Data Science, Business Intelligence, and other teams to enable the next generation of data-driven products and AI capabilities. The ideal candidate combines strong software engineering and data architecture skills with curiosity about machine learning systems and a drive to automate, optimize, and scale data workflows. What You'll Do Design, build, and optimize ETL pipelines that power analytics, data science, and ML workflows using tools such as Databricks, PySpark, and Airflow. Develop and maintain labeling and retraining pipelines for machine learning models, ensuring quality, reproducibility, and observability. Implement and support MLOps practices, including model versioning, CI/CD for ML, and model monitoring in production environments. Collaborate with data scientists to productionize and scale model training, inference, and evaluation pipelines. Contribute to the design and evolution of the data lakehouse, including schema design, partitioning strategies, and performance optimization. Document and communi..
