Data Quality Assurance Engineer

ClassWallet

About The Position

We are seeking a highly skilled and motivated Data Quality Assurance Engineer with experience in business intelligence and data analytics to join our dynamic team. This role will be responsible for ensuring the integrity, accuracy, and reliability of data powering our business intelligence and analytics platform. You will play a critical role in designing and implementing automated testing frameworks, monitoring data pipelines, and collaborating with cross-functional teams to define data quality standards, detect data drift and ensure the quality of our data deliverables.

Requirements

Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field (or equivalent experience).
5+ years of experience in data quality, analytics engineering, data engineering, or QA for data intensive systems.
Experience with data quality or analytics testing frameworks.
Strong command of SQL and experience validating large datasets in data warehouses.
Familiarity with ETL/ELT processes and tools like Airflow or Snowflake.
Experience testing BI dashboards, metrics, and analytical models.
Solid understanding of agile methodologies.
Excellent problem-solving skills with a focus on data profiling and statistical analysis.

Nice To Haves

Experience with cloud platforms (AWS is highly preferred).
Experience integrating data quality checks into CI/CD pipelines.
Working knowledge of Python for data validation or automation.
Familiarity with performance testing tools.
Knowledge of data governance and compliance.
Background in traditional QA automation or SDET roles.

Responsibilities

Design, implement, and maintain automated data quality checks to validate accuracy, completeness, consistency, and timeliness across data pipelines and analytics layers.
Implement proactive monitoring and alerting for data pipelines to detect anomalies, schema changes, or data drift.
Validate business logic, calculations, aggregations and filters in BI tools to ensure alignment with business definitions.
Perform end-to-end testing of data pipelines, including ingestion, transformation (ETL/ELT), semantic layers and reporting outputs.
Conduct regression testing for schema changes, pipeline updates, and BI model modifications.
Integrate data quality checks into CI/CD pipelines and production monitoring.
Monitor data freshness, volume, and distribution to proactively detect and alert on failures or quality degradation.
Partner with data engineering, analytics, and product teams to define data acceptance criteria and quality thresholds.