QA Engineer undertakes a range of responsibilities from testing and verifying data and analytics products to ensure they meet system, functional and business requirements with a particular focus on testing quality of production deployment. The QAE is focused on data processes integration, system and performance testing under the guidance of the SRE. They also work in a cross-functional team with the use case and data platform engineers to co-develop and verify the digital products
- Use Agile engineering practices and various software and web/mobile development technologies to rapidly test digital solutions
- Write and help others write test cases
- Perform acceptance test of user stories on a day-to-day basis
- Perform end-to-end tests in relation to larger releases with external stakeholders
- Drive the squad’s test strategy and test efforts, e.g., taking initiative to automate where possible and coaching others
- Own the process during test periods where functionality is tested across several squads and departments
- Be an active participant in grooming and planning, to ensure that acceptance criteria are testable
- Sit together with designers, SMEs and Journey Owner while user stories are developed, bringing input and helping with the testing angle
REQUIRED EDUCATION AND EXPERIENCE:
- Bachelor’s degree required; Computer Science, MIS, or Engineering preferred
- 5 years of experience working in data engineering or architecture role, 7+ preferred (3 years with 5 preferred for junior role)
- 1+ years of experience contributing to the architecture and design (architecture, design patterns, reliability, and scaling) of new and current data systems, preferably working with large-scale systems and data
- 2+ years of on-the-job experience working with industry recognized analytics data and ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow) with pySpark strongly preferred
Key Skills :
Domain Expertise (required)
- Several years of experience within software development and data engineering with multiple programing languages and tools, preferably Python, JavaScript, and Bash
- Experience with writing and automating test cases with multiple tools including code-best tooling (e.g., xUnit frameworks)
- Knowledge of an Agile software development process
- Good understanding of the E2E data system landscape
- Cloud Infrastructure knowledge with Azure preferred
Domain Expertise (beneficial)
- Familiarity with emerging Data and ML tooling (e.g., Kedro, Dask/Ray, Arrow, Delta Lake/Iceberg, Prefect, Dagster, Kubeflow), DevOps tooling (e.g., Cloud DevOps, Github Actions) and Kubernetes as a developer and/or administrator Experience with distributed SQL engines (e.g., Spark SQL, BigQuery, Athena/Presto)
- Hands-on knowledge of BI and DWH: data modeling (e.g., Star Schema, Data Vault), Cloud-native DWH (Snowflake, Redshift, Synapse Analytics) and SQL DE tool DBT