Data & ML Feature Engineer

Job Title: Senior Data & ML Feature EngineerLocation: Pittsburgh PA/ Cleveland OH/ Dallas TXJob Type: Permanent Full TimeSkills:Apache Hadoop YARNApache KafkaAWS SageMakerBig Data, Analytics & OperationsHadoop HiveMachine LearningPandasPythonDockerPosition Description:We are seeking an experienced Data & ML Feature Engineer with strong expertise in Python and big data technologies to join our team. This role focuses on operational excellence, including optimizing feature engineering pipelines and maintaining machine learning models in production environments. Desired candidate will work closely with platform and data science teams to ensure scalable, reliable, and high-performance ML workflows using existing frameworks.Future Duties and Responsibilities:Design and implement scalable, reusable feature pipelines (batch and real-time)Develop complex feature transformations and advanced data modeling logicOptimize feature performance, latency, and cost efficiencyEnsure feature quality, validation, and SLAs (freshness, accuracy, reliability)Collaborate with Data Science and ML Engineering teams to align features with use casesContribute to feature store architecture and standardsMentor Feature Engineers and promote engineering best practicesSupport production deployment, monitoring, and incident resolutionRequired Qualifications to be Successful in this Role:6–10+ years in Data Engineering, Feature Engineering, or ML EngineeringProven experience designing production-grade data/feature pipelinesStrong track record in scalable distributed data systemsExperience working in enterprise AI/ML platforms or feature storesPrior mentoring or technical leadership experienceTechnical Skills:Programming: Advanced Python and SQLDistributed Processing: Spark / Flink (large-scale data processing)Feature Engineering: Advanced transformations, feature design patternsData Modeling: Complex transformations, aggregation strategiesFeature Stores: Hands-on with platforms such as Hopsworks, Feast, SageMakerML Lifecycle Understanding: Feature importance, model input optimizationData Quality & Validation: Drift detection, validation frameworksPlatform & Engineering:CI/CD pipelines and automated testingCloud platforms (Azure / AWS / GCP)Monitoring, observability, and production debuggingPerformance tuning and scalability optimizationSoft Skills:Technical leadership and mentoringCross-team collaboration (Data Science, MLOps, Platform)Strong problem-solving and optimization mindsetAbility to translate business use cases into feature logic