QA Test Automation Engineer

Responsibilities:

• Develop, execute, and automate tests for data ETL pipelines and API frameworks
• Work closely with the development team to identify gaps in testing
• Data analysis: a key part of test ownership is skill in analyzing and presenting data that is constantly being updated. Familiarity with statistical analysis on large datasets is desired.
• Responsible for geospatial and non-geospatial software verification and validation, including test planning, development, execution, analysis, and associated documentation. Coordinate testing activities for various projects.
• Support bug reporting and tracking and software test processes.
• Implement the team's test automation strategy to meet the release schedule and satisfy security audit requirements.

Experience:

• 2+ Years of experience as a test engineer on enterprise-level applications.
• Experience in testing data pipelines for ingestion, transformation, and loading, preferably using an orchestration tool like Argo Workflows or Apache Airflow.
• Experience using a wide variety of data storage tools and a strong understanding of how to read, write and update them (preference for PostgreSQL, SQLite, AWS S3, Elasticsearch)
• Demonstrated experience in using scripting languages like Python or Bash within data pipelines and CI/CD pipelines.
• Understanding of information governance principles and how they could be applied in a testing capacity.
• Hands-on experience with back-end and front-end languages like Python, Ruby, JavaScript, or Scala.
• Experience integrating test suites with CI/CD tools such as GitHub Actions or ArgoCD.
• Familiarity with Infrastructure as Code (IaC) principles is a plus. We’ll use IaC tools like Terraform to provision infrastructure used in testing.
• Experience with geospatial data is highly desirable.
• Familiarity with risk models and other data constructs in the insurance industry is a plus.