Senior QA Data Testing Engineer

Growth Acceleration Partners•Colorado Springs, CO

About The Position

We are looking for a Senior QA Data Testing Engineer with strong expertise in validating data pipelines, ETL processes, and enterprise Data Warehouse environments. In this role, you will ensure the integrity, reliability, and consistency of the data that feeds analytics and reporting platforms. The primary focus of the position is backend data quality validation, ensuring that datasets entering the Data Warehouse are accurate, complete, and consistent across systems. You will work closely with Data Engineers and Data Analysts to review ETL pipelines, validate data transformations, and identify anomalies within analytical datasets. This role requires a strong analytical mindset and a deep understanding of how data flows across systems and integrations. This position is not focused on BI frontend testing or dashboard validation, but rather on ensuring the data layer powering analytics and reporting systems is reliable and trustworthy.

Requirements

5+ years of experience in QA Engineering with strong focus on data validation and data pipeline testing
Proven experience working with Data Warehouses and analytical data models
Strong experience validating ETL processes and data integrations feeding a Data Warehouse
Experience analyzing large datasets to identify data quality issues, inconsistencies, or anomalies
Experience supporting data validation prior to analytics or reporting consumption
Experience integrating data testing practices within CI/CD workflows
Strong SQL expertise for backend data validation and troubleshooting
Experience working with Data Warehouses and analytical data models
Hands-on experience validating ETL pipelines and data integrations
Experience in Data Quality validation across analytical datasets
Strong Python skills for data manipulation or processing
Deep understanding of data flows across systems and integrations
Strong analytical mindset with Data Engineer / Data Analyst perspective
Experience identifying anomalies and inconsistencies in large datasets
Experience integrating QA validation within CI/CD pipelines
Advanced English proficiency (spoken and written)
Strong analytical and investigative mindset
Excellent documentation and communication skills
Ability to identify risks and proactively address data quality issues
Collaborative mindset when working with data and engineering teams
Strong attention to detail and problem-solving abilities

Nice To Haves

Experience working with Microsoft Fabric
Familiarity with Databricks
Experience with Azure Data Factory or similar orchestration tools
Familiarity with Power BI (to understand downstream data consumption)

Responsibilities

Data Quality & Data Warehouse Validation
Ensure the quality, integrity, and reliability of data entering the Data Warehouse
Validate analytical data models and ensure datasets are consistent and complete
Analyze datasets to detect data quality issues, inconsistencies, and anomalies
Perform reconciliation and cross-validation between source systems and warehouse outputs
Apply data quality principles including completeness, accuracy, consistency, and reliability
ETL & Data Pipeline Testing
Review and validate ETL pipelines and data integrations feeding the Data Warehouse
Test ingestion workflows and transformation logic across data pipelines
Validate data transformations across staging, transformation, and warehouse layers
Perform source-to-target validation across different environments
Ensure pipelines deliver accurate datasets to downstream analytics systems
Backend Data Validation
Write advanced SQL queries to validate data transformations and dataset integrity
Analyze large datasets to identify discrepancies and unexpected patterns
Support validation of structured and semi-structured data pipelines
Conduct root cause analysis for data discrepancies across systems
Automation & Data Validation Tools
Develop automated data validation processes using Python for data processing or manipulation
Implement repeatable validation checks across data pipelines
Support automated testing strategies for ETL and data integrations
Integrate testing processes within CI/CD environments
Cross-Functional Collaboration
Work closely with Data Engineers and Data Analysts to ensure the reliability of analytical data
Support validation and testing before data is consumed by analytics platforms or reporting tools
Collaborate with governance and engineering teams to maintain high data quality standards
Document testing methodologies and validation frameworks