Senior Machine Learning Data Engineer (DataOps), Materra

Overview In this role you build and unify the data foundation powering model training at Materra. You will design automated data ingestion and validation pipelines, establish dataset versioning, and ensure high-quality data for ML workflows. You’ll bridge operations, remote annotation teams, and ML engineers to keep data reliable and well-structured. This work underpins scalable AI for transforming plastics recycling. You will join a mission-driven team focused on reducing global waste through cutting-edge data and automation. Compensation / Benefitsbonusequitybenefits Responsibilities Architect and implement automated ETL/ELT data pipelines to aggregate and harmonize data from diverse sources. Apply Data Ops practices with data quality monitoring, schema validation, and anomaly detection. Standardize and integrate third-party annotation workflows into unified datasets for training. Design and maintain dataset versioning and storage for reproducible ML experiments. Collaborate with ML engineers and operations to translate material, form factor, and sensor metadata into training features. Key requirements Degree in Computer Science, Data Engineering, Software Engineering, or related field 3+ years building scalable data pipelines and unifying fragmented data storage systems Proficiency in Python with data libraries (Pandas, Num Py, SQL)Hands-on experience with Data Ops practices, automated data validation, and dataset versioning Experience structuring datasets for ML workflows, including annotations, metadata tracking, and training set curationcross-functional collaborationclear communicationattention to data quality Google Cloud Platform (Big Query, Cloud Storage, Dataflow, Dataproc, Vertex AI Data Pipelines)Workflow orchestration (Cloud Composer, Airflow, Prefect, Dagster)Multimodal / unstructured data handling (image, sensor metadata, unstructured records)