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# Data Engineer ETL QE

- **Company:** Sysmind LLC
- **Location:** Plano, TX, United States
- **Experience:** Expert
- **Salary:** $124,800.0
- **Contract:** Temporary to permanent
- **Skills:** Application Programming Interfaces (APIs), Automation of Tests, Databases, Extract Transform Load (ETL), Data Mapping, Data Profiling, Data Warehousing, Relational Databases, Database Queries, Dimensional Modeling, Entity Relationship Models, Python (Programming Language), Windows PowerShell, Power BI, Shell Script, SQL Databases, SQL Server Integration Services, Tableau (Software), Talend, Azure Data Factory, Informatica Powercenter, Snowflake, Bug Reporting, AWS Glue
- **Published:** July 30, 2026
- **Apply:** https://www.careerjet.com/jobad/usaf238d655962e975f33b81e71a7d91ff

## About the Role

ETL QA requires strong SQL proficiency, understanding of Data Warehousing concepts (schema, dimensions/facts), and experience with ETL tools (e.g., Informatica, Talend, SSIS). You must also know how to validate data completeness, accuracy, and transformations using automation scripts or data profiling tools
Desirable Skills:
Keyword: ETL
Skills: Data Warehouse BI Testing~Data Warehouse Experience Required: 10+ Years

## Description

Source-to-Target Verification: Ensure no data is lost during the extract, transform, and load phases. Compare row counts and column-level data between source (e.g., flat files, APIs, relational databases) and target systems.Data Transformation Testing: Verify that business rules, data type conversions, aggregations, and filtering logic were applied correctly by the ETL pipeline.Data Quality & Integrity Checks: Monitor for null values, duplicate records, data truncation, and violated constraints (e.g., primary or foreign key failures).Test Documentation: Create detailed test plans, test cases, and bug reports based on Source-to-Target Mapping (STM) documents and business requirements.Report & Dashboard Validation: Verify that the final data pushed to Business Intelligence tools (like Power BI or Tableau) accurately reflects the underlying data warehouse.Essential Technical SkillsSQL Mastery: The ability to write complex queries (joins, subqueries, group by, aggregations) to compare
databases and validate data at every pipeline stage.Data Warehousing Knowledge: Deep understanding of dimensional modeling, fact and dimension tables, star schemas, and snowflake schemas.ETL Tool Familiarity: Hands-on experience or theoretical knowledge of tools used for data movement and transformation (e.g., Informatica PowerCenter, AWS Glue, Talend, Azure Data Factory, SSIS).Scripting Languages: Proficiency in automation, such as Python, Shell Scripting, or PowerShell, for data reconciliation and test automation.Data Modeling & Architecture: Capability to read entity-relationship diagrams (ERDs) and understand database normalization versus denormalization.


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