Role Summary

The ETL QA Engineer is responsible for validating all extract, transform, and load (ETL) processes that support our data operations. This role focuses on end-to-end data verification, sub-file preparation, dataset creation, and ensuring each client’s unique ETL requirements are followed. The engineer collaborates closely with ETL developers, analysts, and product teams to ensure reliable, consistent, and high-quality data movement.

Key Responsibilities

ETL Testing & Data Validation

• Test ETL extract, transform, and load processes to ensure accuracy, completeness, and alignment with requirements.
• Validate source-to-target mappings, business rules, transformation logic, and workflow execution.
• Execute deep-dive data analysis using tools such as Access, Excel, Studio 3T, and occasionally DBeaver or Snowflake.
• Perform reconciliation checks, boundary tests, negative testing, and cross-system comparisons.

Data Analysis & Sub-File Creation

• Build and prepare sub files using Excel, Studio 3T, and Notepad++ based on client specifications.
• Analyze and review dataset outputs to confirm expected results for each ETL process.
• Ensure sub files are structured correctly and formatted consistently across client implementations.

Data Setup & Manipulation

• Perform data setup activities including File Sub creation, App setup, and manual data manipulation using Excel and Studio 3T.
• Validate datasets after setup to ensure they are ready for testing or downstream processes. 

Client-Specific ETL Knowledge Management

• Maintain detailed documentation of client-specific rules, processing logic, file layouts, and ETL behavior.
• Ensure each execution or validation aligns with the client’s unique requirements and variations.
• Assist in maintaining a centralized knowledge base for scalable, repeatable QA work.

API, Cloud, and Workflow Validation (As Needed)

• When applicable, perform AWS checks, including reviewing lambda execution, logs, and triggers.
• Perform API validation using Postman when ETL processes involve API integrations.
• Validate Snowflake queries and cloud data movement when required by the project.
• Access AWS and Snowflake environments sparingly and only when aligned with assigned QA responsibilities.

Test Planning & Documentation

• Create detailed test cases for ETL scenarios, transformations, and data dependencies.
• Document results, defects, and detailed reproduction steps.
• Track testing progress, data preparation steps, and client-specific variations.

Collaboration

• Work with ETL developers to troubleshoot data issues, clarify logic, and validate fixes.
• Coordinate with business analysts to ensure test coverage aligns to client expectations.
• Support both onshore and offshore collaboration, ensuring consistent QA processes across teams.

Required Skills & Qualifications

• 2–5+ years of ETL QA, data testing, or data analysis experience.
• Strong SQL skills, including joins, aggregations, and data validation queries.
• Hands-on experience with Excel, Access, Studio 3T, and general data-analysis tools.
• Experience with data-correction tools and file-manipulation utilities.
• Understanding of ETL concepts, data pipelines, and data mappings.
• Strong analytical and problem-solving skills with high attention to detail.
• Ability to track client-specific processing rules across multiple workflows.

Preferred Experience

• Exposure to Snowflake, AWS, lambda log review, or cloud-based data processing.
• Experience validating API calls with Postman.
• Experience with DBeaver or other SQL clients.
• Experience working with both onshore and offshore QA teams.

Interview stages

1. Call with recruiter
2. Technical Interview
3. Client Interview

Recruiter Natali Andreikiv