Data Engineer II

GoGuardian is a learning technology company developing solutions that support K-12 education, effective teaching, equitable engagement, and student safety. The Data Engineer II will design, build, and improve the analytics and AI/ML data ecosystem, including ETL, MLOps, model training, data lakehouse, and data governance workflows.

Responsibilities:

• 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 communicate data architecture, lineage, and dependencies to ensure transparency and maintainability across teams
• Champion data quality and governance, ensuring that datasets are accurate, well-structured, and compliant with organizational standards
• Leverage infrastructure-as-code and containerization to build reproducible, maintainable environments
• Participate in code reviews and continuous improvement of engineering best practices within the team

Requirements:

• Bachelor's degree in Computer Science, Engineering, or related field
• 2–4 years of experience building and operating large-scale data systems, ideally supporting analytics and ML workloads
• Proficiency in Python and SQL, with experience in PySpark, pandas, or similar data processing frameworks
• Experience with DBT
• Experience with modern data warehousing and lakehouse platforms, preferably Databricks
• Hands-on experience with workflow orchestration tools such as Airflow, Dagster, or Prefect
• Strong understanding of data modeling, ETL design, and distributed data systems
• Experience with AWS data and compute services (S3, Lambda, ECS, CloudWatch, etc.) or equivalent cloud platforms
• Familiarity with MLOps concepts (e.g., feature stores, model registries, CI/CD for ML)
• Experience using Infrastructure as Code, preferably Terraform
• Excellent problem-solving, collaboration, and communication skills; comfortable working in a dynamic, fast-paced environment