Data Science Specialist – Feature Store, ML Platform

Job not on LinkedIn

🕒 August 6

🇧🇷 Brazil – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

👻 Ghost score 19%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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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 Feature Store capabilities: data lineage, feature views, feature recommendation, and new query engines • Design and implement Apache Iceberg tables focused 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 standards for feature quality, versioning, and governance across the platform • Serve as the technical point of contact for data and data science teams consuming the Feature Store

🎯 Requirements

• Proven expertise in feature engineering on enterprise ML platforms (Feast, Tecton, Hopsworks, or equivalent) • Advanced proficiency with 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 • Solid production experience with AWS data services (S3, Glue, EMR, Redshift, Athena) • Nice to have: experience with data lineage and metadata catalogues in production (DataHub, OpenMetadata, Marquez) • Experience with Amazon EMR: cluster configuration, optimization, and Spark job tuning • Expertise in MLOps practices focusing on versioning and traceability of data artifacts • Previous experience in a financial context working with high-cardinality, high-frequency data and regulatory requirements • Familiarity with scalable data quality tools (Great Expectations, Soda, dbt tests)