Role Feature Engineer / ML Data Engineer
Location Cleveland, OH | Pittsburgh, PA | Dallas, TX (Onsite)
Fulltime
Job Description
Must Have Technical/Functional Skills:
10+ years in Data Engineering, Feature Engineering, or ML Engineering
Proven experience designing production-grade data/feature pipelines
Strong track record in scalable distributed data systems
Experience working in enterprise AI/ML platforms or feature stores
Prior mentoring or technical leadership experience
Job Description:
Design and implement scalable, reusable feature pipelines (batch and real-time)
Develop complex feature transformations and advanced data modelling logic
Optimize feature performance, latency, and cost efficiency
Ensure feature quality, validation, and SLAs (freshness, accuracy, reliability)
Collaborate with Data Science and ML Engineering teams to align features with use cases
Contribute to feature store architecture and standards
Mentor Feature Engineers and promote engineering best practices
Support production deployment, monitoring, and incident resolution
Roles & Responsibilities
Technical Skills
Programming: Advanced Python and SQL
Distributed Processing: Spark / Flink (large-scale data processing)
Feature Engineering: Advanced transformations, feature design patterns
Data Modelling: Complex transformations, aggregation strategies
Feature Stores: Hands-on with platforms such as Hops works, Feast, SageMaker
ML Lifecycle Understanding: Feature importance, model input optimization
Data Quality & Validation: Drift detection, validation frameworks
Platform & Engineering
CI/CD pipelines and automated testing
Cloud platforms (Azure / AWS / GCP)
Monitoring, observability, and production debugging
Performance tuning and scalability optimization
Soft Skills
Technical leadership and mentoring
Cross-team collaboration (Data Science, MLOps, Platform)
Strong problem-solving and optimization mindset
Ability to translate business use cases into feature
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