Senior Data Engineer

About the role

Nelo is building a modern alternative to credit cards for consumers across Latin America, and this role is central to that mission. You will own and evolve the core data platform that powers analytics, machine learning, and business decision-making at the company. This is a hands-on, high-impact position where you design, build, and maintain scalable and reliable data pipelines. You will partner closely with Analytics, Product, Engineering, Marketing, Risk, and Machine Learning teams to ensure the data infrastructure meets their needs. Your work will directly enable self-service analysis and robust, scalable data products as Nelo continues to grow. You will balance speed, correctness, and long-term maintainability while operating in a fast-moving startup environment.

Key facts

What you'll do

• Own and evolve the data platform by designing, building, and maintaining scalable, reliable data pipelines and datasets that power analytics, reporting, and machine learning use cases across the company.
• Build and maintain production-grade ETL/ELT pipelines that ingest data from transactional systems, third-party providers, and event streams into our data warehouse and feature store using tools like Airflow, AWS Glue, dbt, or similar orchestration systems.
• Partner with Data Analytics and stakeholders to ensure data is well-modeled, documented, and accessible for self-service analysis, enabling teams to make informed decisions quickly.
• Support machine learning workflows by building and maintaining feature pipelines and feature stores that serve model training, validation, and both online and offline inference requirements.
• Implement data quality checks, monitoring, alerting, and SLAs to ensure trust, reliability, and consistency in our data products as usage and complexity increase.
• Build tooling, abstractions, and CI/CD pipelines that improve the developer experience, making it easier and safer to develop, test, and deploy data pipelines with confidence.
• Collaborate cross-functionally with Software Engineers, ML Engineers, and Product Managers to align data models and pipelines with product and business objectives.
• Continuously improve the performance, cost efficiency, and scalability of data infrastructure to handle growth in data volume and expanding use cases across the organization.
• Ensure strong data observability and reliability by applying best practices for production systems and maintaining clear standards for data health.
• Enable business and analytical teams through well-structured datasets and documentation that reduce friction and accelerate insight generation.
• Work with big data and distributed processing frameworks such as Spark or equivalent technologies to handle large-scale data processing needs.
• Maintain and iterate on feature stores and ML data pipelines to support both offline experimentation and online inference in production environments.

Requirements

• At least 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, infrastructure, and tooling that supports analytics and machine learning.
• Advanced SQL skills and deep experience with data modeling for analytics and ML use cases, including dimensional modeling and schema design.
• Hands-on experience building ETL/ELT pipelines using orchestration tools and frameworks such as Airflow, AWS Glue, dbt, or similar systems.
• Experience working with cloud data warehouses and query engines such as Athena, Presto, Redshift, BigQuery, or Snowflake for scalable analytics workloads.
• Familiarity with big data or distributed processing frameworks such as Spark or equivalent for batch and streaming data processing.
• Experience designing, building, and maintaining CI/CD pipelines for data workflows to ensure reliable and repeatable deployments.
• Exposure to feature stores, ML data pipelines, or close collaboration with ML Engineering teams is a strong indicator of fit for this role.
• Experience with AWS services including S3, IAM, Lambda, Glue, EMR, and related components of cloud ecosystems where data infrastructure runs.
• Strong understanding of data reliability, observability, and best practices for operating production data systems at scale.
• Ability to write clean, maintainable, and well-tested code that other engineers can rely on and extend.
• Proven ability to work cross-functionally with Analytics, ML, and Product teams to deliver data solutions that meet business needs.
• Strong communication skills to explain technical concepts to non-engineers and align on trade-offs between speed, complexity, and maintainability.

Nice to have

• Experience with feature stores, ML data pipelines, or close collaboration with ML Engineering teams is a strong plus.

Practical notes

This role is based in-office in Mexico City or New York City.

You will enjoy 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, and relocation support.