Data Engineer (Airbyte, Redshift, dbt)

We're building a data warehouse for a US-based professional services company: multiple source systems syncing into Redshift via Airbyte, dbt for transformations, with BI reporting on top. Some sources have native connectors, others need custom builds. Timeline is tight, so we need someone who can start quickly and work independently. What you'll do - Set up and manage Airbyte pipelines into Redshift: native connectors (QuickBooks, Salesforce) plus custom connectors for systems that don't have one (REST APIs, expense and time tracking tools) - Build custom Airbyte connectors or Python-based extraction where no connector exists - Handle multi-instance sources (e.g. two instances of the same accounting system merging into one model) - Ingest and normalize Excel-based sources so they behave like proper tables - Write dbt models to stage, clean, and merge source data into a warehouse ready for BI - Own pipeline reliability: monitoring, failure alerts, schema change handling, incremental loads What we need - Hands-on Airbyte experience, including building or modifying custom connectors (CDK or low-code builder) - Strong Python for API extraction: auth, pagination, rate limits, retries - Solid SQL and Redshift experience (or a comparable warehouse) dbt for transformations and testing - You can take an API doc for a system you've never seen and have data landing in the warehouse within days, not weeks - Good written English, some overlap with US Eastern hours Nice to have - AWS fundamentals (IAM, S3, networking around Redshift) - Experience with QuickBooks or Salesforce data models - Worked with agencies or consultancies before How it works Hourly, roughly 15-25 hours/week to start. This is a defined build with a clear scope. If it goes well, there's ongoing work across other projects. To apply Describe a custom connector or API integration you've built: what the source was, how you handled auth and pagination, and how you made it reliable. Generic applications won't be considered.