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Nelo is a profitable fintech company in Mexico founded in 2019 by former Uber international growth leaders. The company offers credit cards, BNPL loans, bill payments, and a marketplace through a mobile-first app. With $100M in annual revenue, $1B annualized GMV, and $140M+ raised, Nelo operates lean with 60 employees and 8 engineers, leveraging AI for underwriting, collections, and operations.
As Senior Data Engineer, you will own and evolve Nelo's core data platform that powers analytics, machine learning, and business decision-making across the organization. This is a hands-on, high-impact role requiring experience across the full data lifecycle—from ingestion and transformation to reliability, scalability, and ML enablement.
Key responsibilities include:
- Design, build, and maintain scalable, reliable data pipelines and datasets powering analytics, reporting, and ML use cases
- Develop production-grade ETL/ELT pipelines ingesting data from transactional systems, third-party providers, and event streams into the data warehouse and feature store
- Partner with Data Analytics and stakeholders to ensure data is well-modeled, documented, and accessible for self-service analysis
- Build and maintain feature pipelines and feature stores supporting model training, validation, and online/offline inference
- Implement data quality checks, monitoring, alerting, and SLAs to ensure trust in data products
- Build tooling, abstractions, and CI/CD pipelines to improve developer experience and safety in pipeline development and deployment
- Collaborate cross-functionally with Software Engineers, ML Engineers, and Product Managers to align data models and pipelines with product and business needs
- Continuously improve performance, cost efficiency, and scalability as data volume and use cases expand
You will partner closely with Analytics, Product, Engineering, Marketing, Risk, and Machine Learning teams. The company values ambition, hard work, intellectual curiosity, ownership (all employees receive equity), customer-first decisions, open communication (quarterly financials and board presentations shared company-wide), and a fast-learning culture with hundreds of monthly experiments.
REQUIREMENTS:
- Minimum 5 years of experience in data engineering, software engineering, or backend engineering roles with significant ownership of production data systems
- 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 such as Airflow, AWS Glue, dbt, or similar orchestration systems
- Experience working with cloud data warehouses and query engines (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
- Experience with AWS (S3, IAM, Lambda, Glue, EMR, etc.) or similar cloud ecosystems
- Strong understanding of data reliability, observability, and best practices for production systems
- Ability to write clean, maintainable, and well-tested code
- 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
- Bonus: Exposure to feature stores, ML data pipelines, or close collaboration with ML Engineering teams
