About the Role The Staff QA Data Engineer is responsible for validating the accuracy, completeness, and reliability of data pipelines and analytical outputs. This role focuses on designing scalable QA frameworks, testing data transformations, and ensuring client-facing data products are correct and aligned to defined business logic.
Your Responsibilities: Data Quality & Validation - Design and implement data validation frameworks to ensure accuracy and consistency across ingestion, transformation, and reporting layers.
- Validate data transformations between OLTP and OLAP systems, including schema, logic, and aggregation correctness.
- Execute end-to-end testing of data pipelines, from raw data ingestion through downstream reporting outputs.
- Develop automated data quality checks to detect anomalies, breakages, and data drift.
Testing & QA Process - Define and maintain test plans and detailed test cases tied to specific data workflows and use cases.
- Document validation logic, QA processes, and testing methodologies for repeatability and auditability.
- Establish standards for data quality metrics and acceptance criteria across datasets and reporting outputs.
Issue Resolution & Cross-Functional Work - Investigate data discrepancies, perform root cause analysis, and resolve pipeline or transformation issues.
- Partner with Data Engineering, Analytics, and Product teams to align on data definitions, transformations, and validation requirements.
- Validate client-facing dashboards, reports, and analytical outputs against source data and business logic.
- Support validation of campaign performance data, reporting outputs, and QBR deliverables.
Your Qualifications: - Bachelor's or Master's degree in Computer Science, Data Analytics, Statistics, Mathematics, or a related quantitative field.
- 3+ years of experience in data QA, data analytics, or data engineering support roles with hands-on responsibility for validating production datasets.
- Advanced SQL skills used for data validation, reconciliation, and anomaly detection across large datasets.
- Experience testing data pipelines and transformations across multiple layers (ingestion, ETL/ELT, reporting).
- Experience validating BI dashboards and reports (e.g., Tableau), including metric definitions and data consistency.
- Experience building or maintaining automated data quality checks using Python or similar scripting languages.
- Working knowledge of data modeling concepts, including differences between OLTP and OLAP systems.
- Experience working with large-scale datasets in cloud data environments (e.g., Redshift, Hive, EMR, or equivalent).
- Experience investigating and resolving data issues through structured root cause analysis.
Preferred Qualifications: - Experience working with transaction-level datasets (e.g., credit/debit card data), loyalty data, or consumer purchase data.
- Experience validating marketing analytics, campaign performance data, or programmatic advertising datasets.
- Familiarity with data quality frameworks, monitoring tools, or testing methodologies used in production data environments.
- Experience in financial services, fintech, or retail analytics environments.
Salary Range: $110,000 • $125,000
Office Hours: 9am • 5:30pm
Benefits for full-time employees of Affinity Solutions begin on the first of the month following your date of hire with a generous employer contribution for medical, dental, and vision. In addition to company paid holidays, wellness time off, other wellness benefits, and employee discounts, you will also get employer paid life insurance and have the option to enroll into an employer-matched 401K Plan. We strongly encourage work/life balance by providing unlimited vacation days, available starting 90 days from your hire date as a team member.
