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Specialisms - Data Engineering - SaaS
In SaaS, the product runs on its own data. Re:Sourced recruits the data engineers who build the pipelines, warehouses and product-analytics foundations that let SaaS teams ship features and prove they actually worked.
The profile
The work sits close to the product: instrumented event pipelines, the warehouse or lakehouse the whole company queries, the data behind product analytics and experimentation, customer-facing in-product analytics, and increasingly the clean data layer that feeds AI features.
The differentiator is data engineering built for the product, events, experimentation and in-product analytics, not just internal BI reporting. Analytics-engineering rigour (dbt, tested models, documented metrics) is what separates a usable data platform from a swamp. Typical stack: dbt, Snowflake or Databricks, Airflow, and streaming.
Where they come from
The pools sit inside the product-led scale-ups (Canva, Atlassian, SafetyCulture, Deputy, Culture Amp) where data is a first-class part of the product. The reliable profile comes from analytics-engineering or product-data backgrounds and thinks in metrics, not just tables.
Senior data engineers in Sydney price at AUD 160-190k base, base only, 25th-75th percentile of accepted offers. Full bands in the Salary Guide 2026.
Submit a brief
30 minute working session. No fee until placement. Replacement inside 90 days.