Feature Engineer (Sr) | - Design and implement scalable, reusable feature pipelines (batch and real-time)
- Develop complex feature transformations and advanced data modeling 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
| Technical Skills Programming: Advanced Python and SQL Distributed Processing: Spark / Flink (large-scale data processing) Feature Engineering: Advanced transformations, feature design patterns Data Modeling: Complex transformations, aggregation strategies Feature Stores: Hands-on with platforms such as Hopsworks, 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 / Google Cloud Platform) 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 logic | - 3 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
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