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Jobs / Dropbox

Data Engineer

Dropbox·Remote - Canada: Select locationsRemotemid

In short

  • →Construyes y mantienes pipelines de datos con Spark y SparkSQL para modelos empresariales.
  • →Gestionas pipelines desde diseño hasta monitoreo, con enfoque en calidad y fiabilidad.
  • →Trabajas con equipos de producto, ciencia de datos y liderazgo para alimentar KPIs clave.

Experiencia en inglés para comunicación con equipos técnicos y documentación.

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What they ask for

  • ✓Experiencia con Spark y SparkSQL en entornos de producción.
  • ✓Capacidad para gestionar pipelines de datos end-to-end.
  • ✓Conocimiento en calidad de datos, pruebas y trazabilidad.
  • ✓Colaboración con equipos multidisciplinarios (data science, producto, análisis).
  • ✓Experiencia con modelos de datos y datamarts.
  • ✓Disponibilidad para rotación de soporte en horario comercial.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

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Role Description As a Data Engineer on Analytics Data Engineering, you will build and operate the pipelines and data models the rest of Dropbox relies on to understand its products and its business. You will own well-scoped pipelines end to end — design, build, test, ship, monitor — with senior engineers alongside you for the harder architectural calls. Your work feeds the datamarts and KPIs used by data science, product, and company leadership, so the quality of what you build is visible quickly. This is a build-oriented team on a modern stack rather than a maintenance role, and a strong place to develop into an engineer who can own a full data domain. Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here . Responsibilities • Build and maintain Spark and SparkSQL jobs that populate company data models • Own well-scoped pipelines end to end, from requirements through deployment, monitoring, and iteration • Contribute to data quality frameworks, testing, and data lineage instrumentation • Partner with data scientists, analysts, product managers, and engineers to turn data needs into durable models • Extend datamarts and data models supporting recurring reporting and analysis across products • Improve the reliability and cost efficiency of existing pipelines, dashboards, and frameworks • Participate in a business-hours on-call rotation and help improve runbooks and alerting <span class="th

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