Role Summary
We are looking for a highly skilled
Data Subject Matter Expert (SME) to lead
test strategy development and perform
exploratory testing across our data pipelines and Azure-based data platform. The ideal candidate has a strong understanding of
data quality assurance,
Azure services, and
data lifecycle, with hands-on experience testing complex ETL/ELT workflows and ensuring data reliability, accuracy, and compliance.
Key Responsibilities
- Define and implement comprehensive data test strategies for cloud-based data platforms (especially Azure).
- Conduct exploratory testing of data pipelines, transformations, and integrations to identify data quality issues and edge-case failures.
- Collaborate with data engineering and analytics teams to understand data flows, ingestion mechanisms, transformation logic, and business rules.
- Design and execute validation for:
- Source to target data completeness and correctness
- Schema evolution handling
- Data transformation logic validation
- Performance and scalability of pipelines
- Build or enhance test automation frameworks and reusable data validation scripts (SQL, PySpark, Python, etc.).
- Monitor and validate data pipeline jobs in Azure Data Factory, Databricks, Synapse, or other Azure components.
- Create test documentation: strategy docs, test plans, traceability matrices, defect reports, and sign-off criteria.
- Provide guidance and best practices around data testing, lineage tracking, data observability, and monitoring.
Required Skills & Experience
- 5+ years of experience in data testing / data QA / data validation roles.
- Proven expertise in defining test strategy and QA processes for data platforms.
- knowledge of Azure Data Services:
- Solid SQL and PySpark skills must be able to write complex queries for validation.
- Experience in exploratory testing of data: identifying anomalies, probing logic errors, working without fully defined test cases.
- Exposure to CI/CD, test automation frameworks, version control (Git), and Azure DevOps or similar tools.
- Understanding of data modeling, data lineage, and data governance principles.
- Investment background is a plusJob Title: Data SME Test Strategy & Exploratory Testing (Azure Data Platform)
- Location: Bangalore
- Experience Level: 5+ years in Data Testing / Data Engineering / QA
- Type: Contract
- Role Summary:
- We are looking for a highly skilled Data Subject Matter Expert (SME) to lead test strategy development and perform exploratory testing across our data pipelines and Azure-based data platform. The ideal candidate has a strong understanding of data quality assurance, Azure services, and data lifecycle, with hands-on experience testing complex ETL/ELT workflows and ensuring data reliability, accuracy, and compliance.
- Key Responsibilities:
- Define and implement comprehensive data test strategies for cloud-based data platforms (especially Azure).
- Conduct exploratory testing of data pipelines, transformations, and integrations to identify data quality issues and edge-case failures.
- Collaborate with data engineering and analytics teams to understand data flows, ingestion mechanisms, transformation logic, and business rules.
- Design and execute validation for:
- Source to target data completeness and correctness
- Schema evolution handling
- Data transformation logic validation
- Performance and scalability of pipelines
- Build or enhance test automation frameworks and reusable data validation scripts (SQL, PySpark, Python, etc.).
- Monitor and validate data pipeline jobs in Azure Data Factory, Databricks, Synapse, or other Azure components.
- Create test documentation: strategy docs, test plans, traceability matrices, defect reports, and sign-off criteria.
- Provide guidance and best practices around data testing, lineage tracking, data observability, and monitoring.
- Required Skills & Experience:
- 5+ years of experience in data testing / data QA / data validation roles.
- Proven expertise in defining test strategy and QA processes for data platforms.
- knowledge of Azure Data Services:
- Solid SQL and PySpark skills must be able to write complex queries for validation.
- Experience in exploratory testing of data: identifying anomalies, probing logic errors, working without fully defined test cases.
- Exposure to CI/CD, test automation frameworks, version control (Git), and Azure DevOps or similar tools.
- Understanding of data modeling, data lineage, and data governance principles.
- Investment background is a plus
