Data Science Specialist – Feature Store & ML Platform
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Job Description
Data Science Specialist at Compass UOL leading Feature Store capabilities development and implementing efficient data processing. Requires expertise in ML platforms and AWS services.
Responsibilities:
- Lead the development and evolution of Feature Store capabilities: data lineage, feature views, feature recommendation, and new query engines;
- Design and implement Apache Iceberg tables with a focus on read performance, versioning, and schema evolution;
- Architect and optimize the serving layer with Redis for real-time features with strict latency SLOs;
- Integrate and optimize Amazon EMR as a query and large-scale processing engine;
- Define and implement feature selection and transformation pipelines with end-to-end traceability;
- Establish standards for feature quality, versioning, and governance across the platform;
- Act as the technical reference for data and data science teams that consume the Feature Store.
Requirements:
- Proven expertise in feature engineering on enterprise ML platforms (Feast, Tecton, Hopsworks, or equivalents)
- Advanced proficiency in Apache Spark / PySpark for distributed processing at scale
- Deep knowledge of Apache Iceberg and lakehouse architectures (comparative experience with Delta Lake and Hudi)
- Expertise in Redis for low-latency feature serving, including cache invalidation strategies and efficient serialization
- Solid production experience with AWS data services (S3, Glue, EMR, Redshift, Athena)
- Preferred:
- Production experience with data lineage and metadata catalogs (DataHub, OpenMetadata, Marquez)
- Experience with Amazon EMR: cluster configuration, cluster optimization, and Spark job tuning
- Expertise in MLOps practices focused on versioning and traceability of data artifacts
- Prior experience in a financial context with high-cardinality, high-frequency data and regulatory requirements
- Familiarity with data quality tools at scale (Great Expectations, Soda, dbt tests).
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