Remote Data & ML Feature Engineer – Credit Risk

Muttdata; Ciudad de México; Híbrido; MXN 670.000 - 1.116.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote-first culture

English lessons

Wellhub stipend

Certifications covered

Food credits

Birthday off

Referral bonuses

Mutters' Trip

Childcare reimbursement

Descripción de la vacante

Muttdata, a remote-first data products and machine learning startup, is hiring a Data Analytics Engineer — Feature Engineering to join our team. You will build and govern features powering our credit risk model using the AI Factory Feature Store standard, collaborating with modeling and platform teams.

Design features with complete metadata, ensure training/production alignment, and follow deployment pipelines.

Formación

• Experience in feature engineering / data engineering on Databricks + Unity Catalog.
• Advanced SQL and Python; data governance, lineage, and quality (DQX).
• Familiarity with naming conventions and metadata standards.

Responsabilidades

• Design transactional and credit behavior features (frequency, recency, amount, digital engagement, delinquency) for the risk model.
• Implement features with complete metadata (description, PKs/FKs, lineage, owner, tags, data lifecycle, temporality) and D1/D2 design to maximize reuse.
• Follow the deployment pipeline (bundle validation → DDLs → PRD), job monitoring, and FinOps practices.
• Ensure consistency between training and production as the model's single source of truth.

Conocimientos

Feature engineering

SQL

Python

Data governance

Herramientas

Databricks

Unity Catalog

Descripción del empleo

Muttdata, a remote-first data products and machine learning startup, is hiring a Data Analytics Engineer — Feature Engineering to join our team. You will build and govern features powering our credit risk model using the AI Factory Feature Store standard, collaborating with modeling and platform teams.

Design features with complete metadata, ensure training/production alignment, and follow deployment pipelines.