Data Analytics Engineer - Feature Engineering - Databricks - #3

🚀 Join Our Remote Data Products & Machine Learning Startup! 🚀

At Muttdata, we build innovative Data Products and Machine Learning solutions that help companies solve complex business challenges. As a fast-growing, remote-first startup, we're passionate about technology, collaboration, and continuous learning.

This opportunity is with a leading multinational beverage company based in Mexico City.

We are looking for a Data Analytics Engineer — Feature Engineering to join our team 🐶🚀. You'll build and govern the features that power our Credit Score financial model, ensuring reliable, reusable, and auditable signals as the raw material for credit risk — under the AI Factory Feature Store standard.

This role works closely with modeling and platform teams, turning transactional and credit behavior data into trustworthy, well-governed features. Attention to detail, strong data governance instincts, and a reuse-first mindset are essential to succeed in this fast-paced, collaborative environment.

• Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.
• Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
• Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
• Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.
• Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.
• Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.

• Amazon Web Services
• Astronomer
• Databricks

• 📊 We are Data Nerds
• 🤗 We are Open Team Players
• 🚀 We Take Ownership
• 🌟 We Have a Positive Mindset

• Design transactional and credit behavior features (frequency, recency, amount, digital engagement, delinquency) for the risk model.
• Implement features with complete standard 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.

• 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.

• Feature Store certification (Fundamentals / Expert) and experience with financial data.
• Experience developing AI agents / agentic infrastructure (e.g. Mosaic AI Agent Framework, agent orchestration, MCP).