Data QA

Role: Data QA
Location: India/Remote
Long-Term Contract Opportunity
Job Description
Key Responsibilities
Design, develop, and execute data quality test plans, test cases, and automated validation frameworks across data pipelines and warehouses
Validate data accuracy, completeness, and integrity throughout ETL processes, data migrations, and integrations between source and target systems
Perform source-to-target data reconciliation and data mapping verification
Identify, document, and track data defects through to resolution, partnering closely with data engineers and analysts
Validate dashboards, reports, and data models to ensure they meet business and accuracy requirements
Establish and promote data quality standards, governance, and best practices across reporting pipelines
Communicate test results, risks, and quality metrics clearly to onshore stakeholders and leadership
Mandatory Requirements
Strong SQL skills — proven ability to write complex queries, joins, and stored procedures for data validation, reconciliation, and analysis
Hands-on data QA / data testing experience — validating ETL pipelines, data warehouses, and large-scale datasets
Data mapping and source-to-target validation experience — particularly during migrations or system integrations
Strong Python skills — for building automated data validation scripts and testing frameworks
Experience testing on large-scale data platforms such as Snowflake, Databricks, or cloud data services (e.g., Microsoft Azure — Azure SQL, Data Factory, Synapse, or Data Lake)
Excellent communication skills — proven ability to clearly present findings, defects, and quality metrics to technical and non-technical audiences across distributed teams
Strong analytical and problem-solving skills — ability to investigate data discrepancies, identify root causes, and validate findings independently
Ownership mindset — ability to take full responsibility for data quality deliverables and work effectively with minimal supervision in an offshore setup
Nice to Have
Domain knowledge of insurance, specifically P&C (Property & Casualty) — policies, premiums, claims, underwriting, and loss data
Experience validating data visualization / BI outputs (Power BI, Tableau, or similar)
Duck Creek experience (policy, billing, or claims platform)
Familiarity with data warehousing / ETL best practices and data governance frameworks
Exposure to test automation tools, CI/CD pipelines, or data quality tools (e.g., dbt tests, Great Expectations)
AI / Machine Learning exposure — familiarity with validating ML workflows or AI-assisted analytics

Work arrangement
Yes