Quality Engineer

Akkodis is seeking a Quality Engineer for a contract job with a client. The role involves defining and maintaining automated test suites, collaborating with engineers to enforce data quality standards, and conducting various testing processes to ensure data integrity.

Responsibilities:

• Define, implement, and maintain automated test suites to validate data pipelines, transformations, and analytical outputs
• Collaborate with data engineers, modelers, and AI engineers to establish and enforce data quality standards and best practices
• Conduct functional, regression, and integration testing of ETL jobs, data ingestion pipelines, and model outputs
• Develop test strategies to validate structured (OLTP/OLAP) and unstructured data (logs, PDFs, etc.)
• Build automated tools and dashboards for data profiling, data drift detection, and quality scorecards
• Identify root causes of data quality issues and coordinate remediation efforts with engineering teams
• Validate data accuracy across Redshift, Postgres, and data marts using SQL and scripting tools
• Ensure traceability from business rules and requirements to test cases and final outputs
• Participate in Agile/Scrum processes, including sprint planning, demos, and retrospectives

Requirements:

• 5+ years of experience in QA or Quality Engineering roles in data-driven environments
• Hands-on experience with test automation frameworks (e.g., MABL, Selenium, PyTest, Great Expectations, dbt tests)
• Proficient in SQL for validating data transformations across platforms like Redshift, Postgres, or Snowflake
• Familiarity with ETL testing, data ingestion pipelines, and data quality validation techniques
• Experience testing and validating BI dashboards and reports, preferably using tools like Tableau
• Exposure to CI/CD practices and integration of tests into deployment pipelines
• Ability to read and write test cases in Python or other scripting languages
• Experience with version control tools (e.g., Git) and JIRA or similar test tracking systems
• Experience testing unstructured data pipelines, including logs, PDF documents, or application telemetry
• Familiarity with data profiling and anomaly detection tools
• Exposure to cloud platforms, especially AWS (S3, Redshift, Glue)
• Understanding of analytical data modeling concepts, including Star Schema and OLAP structures
• Knowledge of testing AI/ML models, particularly for consistency, fairness, or accuracy in predictions