Principal Engineer - Data Engineering
Core
Build and maintain ML-ready scientific data pipelines for experimental measurement data from precision product development instruments.
Role type
Principal Data Engineer (Scientific Data & MLOps)
Builds
Feature engineering pipelines, data quality frameworks, versioned training datasets, and synthetic data infrastructure.
Domain
AI/ML Data Infrastructure, Scientific Instrumentation, Storage Systems
Deliverable
production ML models
Required skills
Python, SQL, Scalable Pipeline Design, Data Quality Principles, Data Versioning & Lineage, ML Data Lifecycle Awareness
Preferred skills
Feature stores, Synthetic data generation, Data Build Tool (DBT), Data Load Tools, Annotation platform integration, Data lakehouse technologies
Technologies
AWS Kubernetes, PortKey, Agent Gateway, LangFuse, AWS Guardrails, Elastic Search, Airflow, Prefect, AWS Glue, Feast, Tecton, DBT, Label Studio, CVAT, Iceberg, Dremio, AWS Lake Formation, Redshift
Responsibilities
Build and maintain reliable, versioned feature engineering pipelines; Design and operate data quality checks for AI training datasets; Build and maintain training data versioning and lineage tracking; Implement data contracts and governance standards; Contribute to real-time sensor data ingestion pipelines; Build pipeline infrastructure for synthetic data generation.
Seniority
Principal, hands-on IC with strategic guidance
