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# Senior Data Engineer

- **Company:** Benjamin
- **Location:** Barcelona, Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Application Integration Architecture, BigQuery, Cloud Computing, Databases, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Github, Python (Programming Language), PostgreSQL, Operational Databases, SQL Databases, TypeScript, Snowflake, Git, Operational Systems, Api Design, Terraform, Webhooks, Docker
- **Published:** July 23, 2026
- **Apply:** https://es.indeed.com/viewjob?jk=1e3098d2d974db71

## About the Role

* Strong data, platform, or backend engineering background. You've operated reliable pipelines in production, not just built them once
 * Strong Python and SQL. Solid grasp of data modeling, incremental processing, and schema evolution
 * Production experience with Snowflake and AWS
 * Software engineering fundamentals: Git, testing, CI/CD, Docker
 * Infrastructure as code (Terraform), a transformation framework (dbt or similar), integrating APIs, databases, files, events, or managed connectors
 * Fluency in observability, failure recovery, security, and cloud cost management
 * Bias toward simple, practical solutions. Strong communication and ownership

Our stack

 * Data: Snowflake, BigQuery, Postgres, dbt
 * Cloud/infra: AWS (GCP), Terraform, Docker
 * Languages: Python, SQL, TypeScript
 * Tooling: Hex, GitHub Actions, Git, Airbyte, Claude

You won't have used every tool here, and you don't need to. Strong engineering fundamentals and experience operating production data systems matter more than matching the stack.

## Description

* Build integrations across product, payment, advertising, and partner systems pulling data from APIs, databases, files, events, and postbacks into one trusted place
 * Own the flow in both directions: pipelines into Snowflake, and reverse ETL back out into the operational systems that run the business
 * Build event-driven, high-frequency ingestion for real-time and async processing. Internal systems, partner postbacks and webhooks at volume
 * Model and transform data with dbt into clean, reusable, well-documented datasets that analytics and ML can trust
 * Take platform problems zero-to-one: turn a gap into production infrastructure, not a one-off script
 * Ship cloud infra as code and CI/CD, own services end-to-end, from schema and API design to deployment and monitoring
 * Monitor production systems for failures, freshness, quality, latency, performance, and cost. Improve how we alert, recover, backfill, and roll back
 * Mentor others and set the team's data engineering standards


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