Staff Data Engineer

Join our Product & Tech team as a Staff Data Engineer. In this role, you will shape the evolution of our data platform, design scalable data solutions, and establish best practices around data architecture and engineering standards. You will collaborate closely with the product team, backend engineers, and business stakeholders, and mentor and support other Data Engineers. This position offers flexible schedules, a selection of free drinks at the office, regular company events, mentoring and career development, the option to work from another country for up to 3 months a year, and a flexible compensation plan.

• Definir y dirigir la visión técnica para nuestra plataforma de datos y arquitectura.
• Diseñar soluciones de datos escalables, confiables y mantenibles que apoyen el crecimiento del negocio.
• Establecer y cultivar las mejores prácticas en torno a la arquitectura de datos, modelado, calidad y estándares de ingeniería.
• This role is ideal for someone with strong skills in Python and SQL, solid software engineering fundamentals, a background in data-intensive product or platform teams where they contributed to production data architectures and pipelines end to end, and the ability to succeed in a dynamic, outcome-focused environment owning the full data cycle from operational to analytical systems
• Advanced SQL skills with PostgreSQL: Complex query design, performance tuning, indexing strategies, and a deep understanding of execution plans for both OLTP and OLAP workloads
• Strong experience with dbt: Data modeling, testing, and documenting analytical datasets, managing environments, and structuring projects for scalability
• Strong grasp of data modeling: Dimensional modeling plus a clear understanding of trade-offs for operational vs analytical schemas
• Hands-on experience with Dagster (or similar orchestration tools): Designing assets/jobs, dependency graphs, scheduling, monitoring, and incident handling for data workflows
• Proficiency in Python for data engineering: Building robust ETL/ELT pipelines, modular libraries, and tooling with strong testing and observability
• Solid knowledge of containerization and orchestration: Docker and Kubernetes for deploying data services, batch jobs, and microservices in production