Principal Engineer - Data Engineering

WD is building the infrastructure behind the AI-driven data economy. The data you will build pipelines for is experimental measurement data from precision product development instruments, not transactional or clickstream data. You will develop expertise in ML-ready scientific data pipelines.

Responsibilities

Build and maintain reliable, versioned feature engineering pipelines that transform raw engineering, sensor, and operational data into structured ML-ready feature sets; Design and operate data quality checks covering completeness, schema consistency, statistical distribution stability, and label accuracy across AI training datasets; Alert the ML team when data quality degrades before it impacts model training; Build and maintain training data versioning and lineage tracking, including early alerting when deployment data diverges from training distributions; Implement and maintain agreed data contracts between upstream data producers and downstream ML consumers; Establish access control and retention practices for AI data assets; Contribute to real-time sensor data ingestion pipelines under technical direction; Build pipeline infrastructure to operationalize synthetic data generation workstreams; Build and maintain the training dataset registry, feature store, and model input validation integrated with the AI platform

Requirements

Bachelor's or Master's degree in AI, Computer Science, Data Engineering, Electrical Engineering, Applied Mathematics, or related field; AI major preferred; strong data engineering fundamentals required; At least 2-3 years demonstrated project experience building end-to-end data pipelines (academic, personal, or internship contexts); Python: strong proficiency, primary pipeline development language; SQL: strong proficiency, complex queries, window functions, data transformation logic; Scalable pipeline design: batch pipeline architecture, reliability, schema management, fault tolerance, pipeline orchestration (Airflow, Prefect, AWS Glue Jobs); Data quality principles: completeness checks, schema validation, distribution stability monitoring; Data versioning & lineage: reproducibility of training data, ability to trace data origin and transformations; ML data lifecycle awareness: understanding of how data pipelines connect to ML model training, including train/test data leakage and label quality risks

Nice to have

Feature store (Feast, Tecton); Synthetic data generation (VAE, GAN, diffusion models); Data Build Tool (DBT); Data Load Tool (DLT); Annotation platform integration (Label Studio, CVAT); MES / LIMS system integration; Active learning data loop design; Data lakehouse (Iceberg, Dremio, AWS Glue, AWS Lake Formation) / Redshift