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.