We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (18000+ experts across 36 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!

We are looking for a QA Data Engineer who will ensure data accuracy throughout the entire lifecycle, from backend ETL pipelines to frontend visualization. This position requires a hybrid approach, combining thorough manual data reconciliation with the implementation of automated testing frameworks for enterprise data platforms. Responsibilities include verifying that BI dashboard numbers match the source data, ensuring accuracy, completeness, and consistency of data as it moves through pipelines. The ideal candidate will possess a strong understanding of data architecture, manual exploratory testing, and automated validation at scale, along with the business acumen to confirm that data aligns with strategic KPIs and operational needs.

Core Responsibilities

• Design and execute automated test plans for ETL/ELT pipelines to verify data transformations and schema integrity.
• Perform source-to-target mapping and reconciliation checks across data lakes, warehouses, and reporting layers.
• Implement data-specific automation using AWS DQDL framework.
• Validate performance of large-scale pipelines and identify bottlenecks in data processing runtimes.
• Conduct regression testing to ensure new ETL code deployments do not disrupt existing metrics or KPI calculations.
• Investigate data quality incidents end-to-end and provide technical feedback to engineers and product owners.

Required Skills & Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related technical field.
• 4-7 years of experience in data quality assurance.
• Advanced proficiency in SQL, including complex queries, joins, window functions, and stored procedure validation.
• Proficiency in Python for scripting automated validation checks.
•  Experience with cloud data warehouses (Iceberg, AWS Redshift, etc.) and file formats such as Parquet and JSON.
• Hands-on experience validating mappings and data lineage in ETL processes.
• Basic understanding of integrating tests into GitHub Actions workflows.
• Strong knowledge of relational databases and SQL for data validation. 
• Experience with RESTful API testing tools like Postman.
• High attention to detail, critical thinking, and strong written and verbal communication skills.
• Familiarity with BI tools like Tableau for dashboard validation.

Must have Skills: SQL (Strong), Python (Capable), ETL Testing