ML/Data Engineer

What You'll Do

• Build and maintain the data pipelines that feed ML inference systems running in production.
• Support Data Scientists by supplying, preparing, and validating data during model development (without owning model training).
• Design and operate data processing jobs on Dataflow and Dataproc (Spark) for large-scale transforms.
• Own data lake structure and organization in Cloud Storage to support downstream ML and inference consumers.
• Build and maintain orchestration workflows (Cloud Composer/Airflow) that reliably schedule and monitor pipeline execution.
• Implement data quality checks and testing frameworks across pipelines.
• Contribute to feature engineering pipelines supporting a specific upcoming project (feature store tooling see note below).

Required Experience

• 7+ years of software engineering experience, with 3+ years specifically in data infrastructure.
• Strong, hands-on expertise with Google Cloud Platform's data and ML infrastructure stack: Dataflow, Cloud Storage, Cloud Composer, Dataproc.
• Deep expertise in Spark for large-scale data processing.
• Proficiency in Python and SQL.
• Experience building data quality and testing frameworks.
• Experience with pipeline orchestration tools (Airflow, Dagster).

Preferred / Project-Dependent

There is a specific upcoming project where feature store experience may be required. Confirm current need before screening candidates against this line item.

• Vertex AI Feature Store experience, or strong transferable experience with Feast or Tecton (feature versioning, online/offline serving parity).
• Experience with data versioning tools.