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About the role
Data QE Must Have: • Experience of driving advanced software testing techniques applying modern automation test approaches in Data Engineering project, ensuring robust data quality • Hands-on experience of developing BDD based Automation Frameworks for Data, ETL & Event -Driven applications using Python, Java or Typescript • Ability to solve complex automation use cases for new Data Products built on Data -Mesh, Lakehouse and streaming architecture • Working experience of modern data & event -driven technologies, such as: • Testing & Automation: GreatExpectations etc PyTest , Cucumber, Behave, DBT, GCP DVT, Monte Carlo, Soda, Deequ , RestAssured • Data Engineering & Orchestration: BigQuery , Spanner, Apache Kafka,Airflow, Spark, Cloud Composer, DAGs, Apache Beam, Pub/Sub, Dataflow, DataStage, Teradata, Snowflake, ETL, SQL etc • Experience of Data Governance & Visualisation tools like: Looker, Ataccama , Dataplex , Collibra, PowerBI • Strong programming proficiency in one of following coding language - Python (preferable), Java or JavaScript • Experience of designing & executing complex automation testing strategies and frameworks for Functional and Non -Functional requirements of Data and AI platforms • Create or accurately request complex test data, referential integrity, data quality and governance taking into account •I Incorporate automated tests into the CI/CD pipeline and DevOps tooling like Jenkins, Harness, Terraform, Dynatrace etc • Experience with Agile tools (Jira, Confluence, Xray) • Experience on Source code management tools (Git, GitHub) Good to Have: • AI/ Analytics: Exposure to TensorFlow, PyTorch , Scikit -learn, OpenCV, LangChain and GenAI tools • DevOps tooling experience like K8, Docker, Terraform, Jenkins, HarnessRole & responsibilities Preferred candidate profile
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