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Applaudo is seeking an experienced Data Engineer to build reliable integrations and transform complex data into accurate, actionable information. The role focuses on ETL pipelines using TypeScript/Node.js and Python, integrating Zuora REST APIs with a GCP data warehouse, and ensuring data quality and reliability across the stack.
You will collaborate asynchronously with an international team, own monitoring and troubleshooting, and contribute to scalable data movement within a modern cloud
You are an experienced Data Engineer who enjoys building reliable integrations and transforming complex data into accurate, accessible, and actionable information. You have hands-on experience developing production ETL pipelines with TypeScript/Node.js and Python, integrating REST APIs, and working with cloud-based data platforms. You are comfortable joining an established codebase, following existing technical patterns and CI/CD workflows, and becoming productive with minimal guidance.
You communicate clearly in distributed environments, work effectively with limited overlap, and take ownership of monitoring, troubleshooting, and maintaining the reliability of your pipelines.
We Are Engineered Different.
At Applaudo, talented people design, build, and scale meaningful, AI-powered solutions that create real business impact. As an AI-native organization, we collaborate across design, development, cloud, data, and artificial intelligence to turn ideas into scalable products that transform how companies operate, make decisions, and grow.
We are building a high-performance culture grounded in five values: Empowering Excellence, Collaborative Teamwork, Unsolicited Respect, Consistent Transparency, and Efficient Communication. These define how we work, how we support one another, and how we hold ourselves accountable.
Applaudo is a place for people who want to learn fast, take ownership, and work alongside strong teams they are proud to belong to. Joining us means being part of an organization that is evolving intentionally, investing in modern ways of working, and leading AI-native transformation at scale.