What you’ll do

• Own and evolve the data platform: Design, build, and maintain scalable, reliable data pipelines and datasets that power analytics, reporting, and machine learning use cases across the company.
• Build and maintain ETL/ELT pipelines: Develop production-grade pipelines that ingest data from transactional systems, third-party providers, and event streams into our data warehouse and feature store.
• Enable analytics and business teams: Partner with Data Analytics and stakeholders to ensure data is well-modeled, documented, and accessible for self-service analysis.
• Support machine learning workflows: Build and maintain feature pipelines and feature stores that support model training, validation, and online/offline inference.
• Ensure data quality and reliability: Implement data quality checks, monitoring, alerting, and SLAs to ensure trust in our data products.
• Improve developer experience: Build tooling, abstractions, and CI/CD pipelines that make it easier and safer to develop, test, and deploy data pipelines.
• Collaborate cross-functionally: Work closely with Software Engineers, ML Engineers, and Product Managers to align data models and pipelines with product and business needs.
• Scale for growth: Continuously improve performance, cost efficiency, and scalability of our data infrastructure as data volume and use cases expand.

Role requirements

At least 5 years of experience in data engineering, software engineering, or backend engineering roles with significant ownership of production data systems.

Technical Skills

• Strong proficiency in Python for building data pipelines and infrastructure.
• Advanced SQL skills and deep experience with data modeling for analytics and ML use cases.
• Hands-on experience building ETL/ELT pipelines using tools or frameworks such as Airflow, AWS Glue, dbt, or similar orchestration systems.
• Experience working with cloud data warehouses and query engines such as Athena/Presto, Redshift, BigQuery, or Snowflake.
• Familiarity with big data or distributed processing frameworks such as Spark (or equivalent).
• Experience designing and maintaining CI/CD pipelines for data workflows.
• Exposure to feature stores, ML data pipelines, or close collaboration with ML Engineering teams is a strong plus.
• Experience with AWS (S3, IAM, Lambda, Glue, EMR, etc.) or similar cloud ecosystems.

Engineering Mindset

• Strong understanding of data reliability, observability, and best practices for production systems.
• Ability to write clean, maintainable, and well-tested code.

Collaboration & Communication

• Proven ability to work cross-functionally with Analytics, ML, and Product teams.
• Strong communication skills to explain technical concepts to non-engineers and align on trade-offs.

Why you’ll succeed:

• You enjoy owning systems end-to-end and making them better over time.
• You balance speed, correctness, and long-term maintainability.
• You’re excited about enabling others (analysts, scientists, and engineers) to move faster with high-quality data.
• You’re comfortable operating in a fast-moving startup environment with evolving requirements.

Location:

This role is based in-office in Mexico City or NYC

Compensation and Benefits:

• Very competitive salary and equity
• 100% medical, dental & vision insurance coverage for you
• Unlimited PTO
• 401(k) for US-based employees
• Extended maternity and paternity leave
• Relocation support