ML Data Infrastructure Engineer

Redolent

United States

United States

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Über

Key Responsibilities: Design and implement scalable data processing pipelines for ML training and validation Build and maintain feature stores with support for both batch and real-time features Develop data quality monitoring, validation, and testing frameworks Create systems for dataset versioning, lineage tracking, and reproducibility Implement automated data documentation and discovery tools Design efficient data storage and access patterns for ML workloads Partner with data scientists to optimize data preparation workflows Technical Requirements:
7+ years of software engineering experience, with 3+ years in data infrastructure Strong expertise in GCP's data and ML infrastructure:
BigQuery for data warehousing Dataflow for data processing Cloud Storage for data lakes Vertex AI Feature Store Cloud Composer (managed Airflow) Dataproc for Spark workloads
Deep expertise in data processing frameworks (Spark, Beam, Flink) Experience with feature stores (Feast, Tecton) and data versioning tools Proficiency in Python and SQL Experience with data quality and testing frameworks Knowledge of data pipeline orchestration (Airflow, Dagster) Nice to Have:
Experience with streaming systems (Kafka, Kinesis) Experience with GCP-specific security and IAM best practices Knowledge of Cloud Logging and Cloud Monitoring for data pipelines Familiarity with Cloud Build and Cloud Deploy for CI/CD Experience with streaming systems (Pub/Sub, Dataflow) Knowledge of ML metadata management systems Familiarity with data governance and security requirements Experience with dbt or similar data transformation tools

United States

Sprachkenntnisse

English

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