About the role
structured by ORIKey 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…
What you will do
- 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
What they are looking for
- 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.
Nice to have
- Good to have understanding of DSP or AdTech data models
Full posting text
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
Seniority level: Entry level
Employment type: Full-time
Job function: Quality Assurance
Industries: IT Services and IT Consulting