Data Testing Engineer

XPT Software Australia; City of Melbourne; On-site; AUD 120,000 - 180,000; Full time

Job summary

XPT Software Australia in Melbourne is seeking a Senior Data Testing Engineer to own cloud data platform regression and ensure data quality for near real-time analytics. You will leverage Python and SQL to automate large-scale transactional reconciliation and validate data availability, with hands-on work on GCP services such as BigQuery and Cloud Run.

The role requires understanding of data warehousing, ETL processes, data transformations, CI/CD deployments, and experience with graph databases

Qualifications

• Strong Python development and SQL for data validation.
• Experience with data warehousing, ETL processes and CI/CD deployments.
• Hands-on with graph databases and Cypher query language.

Responsibilities

• Implementing cloud platform regression on GCP using Workflows, BigQuery, Cloud Run, Cloud Build, and Cloud Scheduler.
• Automate large-scale transactional reconciliation with Python and SQL.
• Validate data availability for near real-time data.
• Understand data warehousing, ETL, data transformations, and CI/CD deployments.
• Experience with graph databases and Cypher query language.

Skills

Python

SQL

BigQuery

GCP

CI/CD

Data Warehousing

ETL

Cypher

Regression Testing

Data Quality

Job description

Job description

Senior Data Testing Engineer

• Implementing cloud platform regression, preferably on GCP using Workflows, BigQuery, Cloud Run, Cloud Build, and Cloud Scheduler.
• High proficiency in Python and SQL, including the use of related libraries to automate large-scale transactional reconciliation and validate data availability for near real-time data.
• Good understanding of data warehousing, ETL processes, data transformations, and CI/CD deployments.
• Experience working with graph databases, with hands‑on knowledge of the Cypher query language.
• Skilled in implementing regression for high-volume transactional data across multiple data layers, ensuring validations for accurate data quality outcomes and system stability.