Senior ML Data Engineer

Job Summary: 84.51° is a retail data science, insights and media company that helps create personalized experiences for shoppers. They are seeking a Senior ML Data Engineer to architect, build, and operate critical data infrastructure for machine learning models, ensuring production-grade reliability and scalability. Responsibilities: • Feature Store Operations & Governance (40) • Own the feature request lifecycle from intake through deployment, driving reusability and maintaining a searchable feature catalog • Design and build scalable feature pipelines that compute features from diverse sources (BigQuery, Azure Data Lake) and write to Feature Store infrastructure (Vertex AI Feature Store + BigQuery) • Build streaming feature engineering pipelines using Apache Beam/Dataflow for real time feature computation and low-latency model serving with sub-second data freshness • Ensure point-in-time correctness and online/offline feature consistency to prevent data leakage • Implement drift detection, data quality monitoring, and alerting mechanisms • Develop self-service tools and templates that enable teams to independently create features • Training & Evaluation Data Pipelines (30) • Build automated pipelines that generate ML-ready training datasets by combining features with labeled target variables • Implement point-in-time correctness logic and sophisticated sampling strategies to ensure balanced, representative datasets • Maintain comprehensive dataset versioning for full traceability across model versions • Generate detailed evaluation reports with performance metrics segmented by business dimensions • Support operations across both Azure and Vertex AI environments during platform migration • ML Data Operations & Reliability (20) • Serve as Tier 2/3 on-call responder for feature data quality incidents, diagnosing and resolving pipeline failures and performance issues • Maintain comprehensive lineage tracking and metadata management for full data traceability • Support regulatory compliance through proper data governance and documentation • Standards, Education & Collaboration (10) • Establish and enforce feature naming conventions, data quality thresholds, and point-in-time correctness patterns • Conduct workshops on feature engineering best practices and provide expert guidance on feature design • Partner with Data Scientists, ML Engineers, Data Engineering, and MLOps teams to optimize infrastructure and align with technical strategy Qualifications: Required: • 3+ years of hands-on experience building and maintaining ML data pipelines in production environments with demonstrated expertise in scaling and reliability • Expert-level SQL skills and advanced Python programming capabilities with experience in data processing frameworks and ML libraries • Proven experience with cloud data platforms, with strong preference for GCP ecosystem including BigQuery, Dataflow, Vertex AI Feature Store, and associated ML services • Deep understanding of end-to-end ML workflows including training data preparation, model evaluation methodologies, and serving infrastructure requirements • Production operations mindset with experience in monitoring, alerting, on-call responsibilities, and meeting SLA commitments Preferred: • Hands-on experience with Feature Store platforms such as Vertex AI Feature Store, Feast, Tecton, or similar enterprise solutions • Deep knowledge of point-in-time correctness principles, temporal joins, and time-series data modeling best practices • Multi-cloud experience with both Azure and GCP platforms, including data migration and hybrid cloud architectures • Strong familiarity with core ML concepts including feature engineering, label creation, train/test/validation splits, and data leakage prevention • Background spanning both analytics engineering and ML-specific data engineering with understanding of the unique requirements of each domain Company: 84.51° helps companies create sustainable growth by putting the customer at the center of everything. Founded in 2015, the company is headquartered in Cincinnati, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

Created: 2026-03-09