Data Engineer

Join our team as a Data Engineer, where you will be responsible for designing, building, and maintaining data pipelines that power AI model training, validation, and regulatory workflows for autonomous surgical robotics systems. You will work closely with the Data Operations Analyst and collaborate with cross-functional teams to ensure data quality, traceability, and compliance requirements are met. The ideal candidate will have 2+ years of experience in data engineering or a related technical role, strong proficiency in SQL and Python, and familiarity with cloud infrastructure and containerized workflows.

• Conception, construction, and maintenance of data pipelines in Dagster to facilitate AI model training and regulatory workflows.
• Collaboration with the Data Operations team to ensure pipeline outputs meet data quality, traceability, and compliance requirements.
• Troubleshooting and resolution of pipeline failures, performance bottlenecks, and data inconsistencies.
• The ideal candidate brings strong fundamentals in SQL and Python; and is eager to deepen their data engineering expertise in a fast-paced, regulated environment
• 2+ years of experience in data engineering, software engineering, or a related technical role
• Effective communication skills for working closely with analysts, ML engineers, and cross-functional teams
• Familiarity with version control (Git) and collaborative development workflows
• Eagerness to learn and grow in data engineering, including orchestration frameworks, data modeling, and infrastructure-as-code
• Bachelor's degree in Computer Science, Data Science, Software Engineering, or a related field, or equivalent practical experience
• Experience with cloud infrastructure (AWS preferred) and containerized workflows
• Strong proficiency in Python for building data pipelines and automation
• Strong proficiency in SQL for data transformation and analysis
• Experience with Dagster or similar orchestration frameworks (Airflow, Prefect)
• Experience with data warehouse or lakehouse patterns (e.g., Snowflake, Delta Lake, dbt)
• Familiarity with machine learning data lifecycle concepts (dataset versioning, data drift monitoring, model validation datasets)
• Knowledge of DICOM, medical imaging data standards, or ophthalmic imaging modalities (OCT, microscopy) is a plus
• Exposure to regulated environments (FDA, ISO 13485, IEC 62304) or medical device industry