AWS Data Science – Specialist

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🕒 May 21

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Compass

WebsiteLinkedInAll Job Openings

10,000+ employees

🏠 Real Estate

📱 Media

Real Estate • Media

Compass is a real-estate-focused content and services site that provides detailed market analysis, buying/selling/renting guides, mortgage and financing information, and home improvement and renovation advice. The site offers resources for homebuyers, sellers, renters, agents, and real estate investors — including articles on appraisals, affordable housing, investment strategies, staging and property maintenance. Compass aims to help users make informed decisions across the housing lifecycle through timely market updates and practical how-to content.

📋 Description

• Lead the development and evolution of the 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 meeting 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 quality, versioning, and governance standards for features across the platform; • Serve as the technical reference for data and data science teams that consume the Feature Store.

🎯 Requirements

• Expertise in feature engineering on enterprise ML platforms (Feast, Tecton, Hopsworks, or equivalents); • Advanced proficiency in Apache Spark/PySpark for large-scale distributed processing; • Deep knowledge of Apache Iceberg and lakehouse architectures (including comparisons with Delta Lake and Hudi); • Expertise in Redis for low-latency feature serving, including cache invalidation strategies and efficient serialization; • Strong production experience with AWS data services (S3, Glue, EMR, Redshift, Athena); • Preferred: experience with data lineage and metadata catalogs (DataHub, OpenMetadata, Marquez) in production; experience with Amazon EMR including cluster configuration, cluster optimization, and Spark job tuning; expertise in MLOps practices focused on versioning and traceability of data artifacts; prior work in financial contexts with high-cardinality, high-frequency data and regulatory requirements; familiarity with scalable data quality tools (Great Expectations, Soda, dbt tests).