Customer Support Developer (databases)

Airbyte Airbyte · Data AI · United States · Technical Support

This role is a Customer Support Developer focused on databases for Airbyte, an open-source data movement platform. The role involves hands-on development with Java and Kotlin, contributing to open-source connectors, debugging complex database issues, and supporting customers. While the company is building AI-native infrastructure and mentions leveraging AI tools, the core of this role is data integration and customer support, not direct AI/ML model development.

What you'd actually do

  1. Serve as the primary technical escalation point for database-related customer issues, responding via email, ticketing systems, and video calls with precision and clarity.
  2. Triage and prioritize incoming issues with urgency, consistently meeting SLA targets while leveraging AI tools to work smarter and resolve issues faster.
  3. Dig into complex issues across relational databases, data warehouses, data lakes, and cloud object storage, not just finding workarounds but partnering with Engineering to root-cause and drive real fixes.
  4. Reproduce customer-reported bugs by inspecting connector logs, query plans, replication slots, transaction logs, sync state, and destination table state, and document findings with enough technical detail for Engineering to act on immediately.
  5. Contribute directly to Airbyte's open-source database connectors and the Java/Kotlin Bulk CDK: fix bugs, improve error handling, harden schema-evolution and CDC paths, and submit pull requests to the Airbyte repository.

Skills

Required

  • Java
  • Kotlin
  • database troubleshooting
  • data integration
  • customer support
  • debugging
  • SQL
  • data warehouses
  • data lakes
  • cloud object storage
  • CDC implementation
  • schema evolution
  • open-source contributions

Nice to have

  • AI tools
  • AI-assisted workflows

What the JD emphasized

  • shipping contributions to our open-source database source and destination connectors
  • directly shaping how customers move mission-critical data
  • getting your hands dirty in the code
  • root-cause and drive real fixes
  • document findings with enough technical detail for Engineering to act on immediately
  • fix bugs, improve error handling, harden schema-evolution and CDC paths, and submit pull requests