JOb Responsibilities:
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
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