Key Responsibilities
- Design, develop, and execute data validation for large-scale data pipelines
- Collaborate with data teams to understand data flow and transformations
- Schema validation (types, nullability, constraints)
- Anomaly/outlier detection (statistical thresholds, freshness checks)
- Regression testing on transformation logic after schema/pipeline changes
- Contribute to AI-assisted QA initiatives
Skills
- 1-2 years of experience in Data QA.
- Strong hands-on expertise in SQL (tools to mention).Working knowledge of Cloud Systems (AWS, GCP etc).
- Working knowledge of Python for scripting, data manipulation, and automation integration
- Experience with orchestration or logging tools (like Airflow, Dagster, Cloudwatch etc.) enough to trace failures
- Exposure of AI Tools
- Familiarity with CI/CD tools (Jenkins, GitHub)
- Strong analytical mindset, attention to detail, and ability to troubleshoot complex data issues.
- Good to have understanding of DSP or AdTech data models
Skills: qa,gcp,python,github,api testing,ai tools,airflow,sql,aws,cloudwatch,jenkins
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