QA Data Engineer
Position Summary
We are seeking a detail-oriented QA Data Engineer to validate data quality, accuracy, and completeness across enterprise data platforms. The ideal candidate will have strong SQL skills, experience validating large datasets, and the ability to identify discrepancies by comparing source-to-target data against documented business requirements and acceptance criteria.
This role will work closely with Data Engineers, Analytics Engineers, Business Analysts, and stakeholders to ensure data pipelines and reporting outputs meet expected quality standards before release.
Key Responsibilities
Data Validation & Quality Assurance
. Validate data accuracy, completeness, and consistency across source systems, data warehouses, and reporting layers.
. Develop and execute SQL queries to verify data transformations and business rules.
. Compare source and target datasets to identify missing, duplicate, or incorrect records.
. Perform reconciliation testing across multiple datasets and environments.
. Investigate and document data discrepancies, root causes, and remediation recommendations.
Acceptance Criteria Validation
. Review business requirements, user stories, and acceptance criteria.
. Translate business requirements into data validation test cases.
. Verify that data outputs meet expected business rules and reporting requirements.
. Work with stakeholders to clarify requirements and expected results.
Data Pipeline Testing
. Validate data movement through ETL/ELT pipelines.
. Monitor pipeline outputs and identify failures, anomalies, or unexpected variances.
. Perform regression testing following data pipeline changes.
. Support UAT and production validation activities.
Defect Management
. Document data quality issues and defects with supporting SQL evidence.
. Partner with Data Engineers and developers to resolve data-related issues.
. Retest fixes and validate resolution prior to deployment.
Documentation & Reporting
. Create and maintain test cases, validation scripts, and reconciliation documentation.
. Produce data quality reports and testing summaries for stakeholders.
. Contribute to data governance and quality assurance best practices.
Required Qualifications
Must-Have Skills
. Strong SQL skills with the ability to:
o Write complex joins
o Use aggregations and window functions
o Perform source-to-target validation
o Conduct data reconciliation and variance analysis
. Experience validating large datasets in a data warehouse or reporting environment.
. Ability to identify data issues and troubleshoot discrepancies.
. Experience reviewing and validating against acceptance criteria and business requirements.
. Understanding of ETL/ELT concepts and data pipeline workflows.
. Strong analytical and problem-solving skills.
. Excellent attention to detail.
Preferred Qualifications
. Experience working with cloud data platforms such as AWS, Azure, Databricks, Snowflake, Redshift, BigQuery, or Synapse.
. Experience with dbt, Airflow, Data Factory, or other modern data stack tools.
. Experience in Data Quality, Data Governance, Analytics Engineering, or Data Testing roles.
. Familiarity with Agile/Scrum delivery environments.
. Basic Python scripting skills.
Nice-to-Have Backgrounds
Candidates may come from:
. Data QA Engineer
. Data Validation Analyst
. Data Quality Analyst
. Analytics Engineer
. BI Developer with strong SQL validation experience
. Data Engineer with testing and reconciliation responsibilities
. ETL Tester
. Data Warehouse Tester
. QA Analyst specializing in data platforms
Sample Interview Topics
. Write SQL queries to validate source vs target tables.
. Identify data discrepancies using acceptance criteria.
. Explain how to validate a newly deployed data pipeline.
. Investigate row count mismatches between systems.
. Validate aggregation logic used in reports and dashboards.
. Analyze root causes of missing or duplicated records.
