Sr. ML Data Engineer, Relevancy Sciences - Personalization & Loyalty Strategy (P508) The Relevancy Sciences Team is responsible for creating relevant and personalized customer experiences for Krogers E-commerce platform, which ranks among the top 10 ecommerce companies in the US. We generate trillions of recommendations at scale and deliver them to millions of Kroger customers daily. Our team maintains a comprehensive portfolio of machine learning solutions for search & product recommendations. We are seeking a talented and experienced Senior ML Data Engineer to join our data science team, with specialized expertise in building search and recommender systems.Role OverviewYou will architect, build, and operate the critical data infrastructure that powers our machine learning models, spanning from feature engineering to training data generation. This role serves as the bridge between ML requirements and production data systems, with ownership of feature stores, training/evaluation pipelines, and ML-specific data operations. You will enable data scientists to iterate rapidly while ensuring production-grade reliability and scalability.What Youll Do Feature Store Operations & Governance (40%)Own the feature request lifecycle from intake through deployment, driving reusability and maintaining a searchable feature catalogDesign 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 freshnessEnsure point-in-time correctness and online/offline feature consistency to prevent data leakageImplement drift detection, data quality monitoring, and alerting mechanismsDevelop 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 variablesImplement point-in-time correctness logic and sophisticated sampling strategies to ensure balanced, representative datasetsMaintain comprehensive dataset versioning for full traceability across model versionsGenerate detailed evaluation reports with performance metrics segmented by business dimensionsSupport 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 issuesMaintain comprehensive lineage tracking and metadata management for full data traceabilitySupport 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 patternsConduct workshops on feature engineering best practices and provide expert guidance on feature designPartner with Data Scientists, ML Engineers, Data Engineering, and MLOps teams to optimize infrastructure and align with technical strategyWhat Were Looking ForRequired Qualifications:3+ years of hands-on experience building and maintaining ML data pipelines in production environments with demonstrated expertise in scaling and reliabilityExpert-level SQL skills and advanced Python programming capabilities with experience in data processing frameworks and ML librariesProven experience with cloud data platforms, with strong preference for GCP ecosystem including BigQuery, Dataflow, Vertex AI Feature Store, and associated ML servicesDeep understanding of end-to-end ML workflows including training data preparation, model evaluation methodologies, and serving infrastructure requirementsProduction operations mindset with experience in monitoring, alerting, on-call responsibilities, and meeting SLA commitmentsStrongly Preferred Qualifications:Hands-on experience with Feature Store platforms such as Vertex AI Feature Store, Feast, Tecton, or similar enterprise solutionsDeep knowledge of point-in-time correctness principles, temporal joins, and time-series data modeling best practicesMulti-cloud experience with both Azure and GCP platforms, including data migration and hybrid cloud architecturesStrong familiarity with core ML concepts including feature engineering, label creation, train/test/validation splits, and data leakage preventionBackground spanning both analytics engineering and ML-specific data engineering with understanding of the unique requirements of each domainSuccess Indicators:Improved Data Science Productivity:Data Scientists spend significantly less time on data preparation and infrastructure concerns, enabling more focus on model development and experimentationIncreased Feature Reuse:Measurable increase in feature reuse across multiple models and teams, reducing redundant development effort and improving consistencyReliable Automation:Training and evaluation data generation processes operate reliably with minimal manual intervention and high uptimeEfficient Incident Response:Data quality incidents are triaged quickly with clear escalation paths and rapid resolution timesAccelerated ML Iteration:Overall ML model development and iteration velocity improves measurably across all teams using the platform#LI-SSS