Data Engineer QA Data Warehouse / Lakehouse

Global Solutions - GS Om...

Key Responsibilities

• Design and implement automated data validation and reconciliation processes.
• Develop test cases for ETL pipelines, data ingestion, and transformation logic.
• Perform root cause analysis for data discrepancies and anomalies.

Testing & Monitoring

• Conduct unit, integration, and regression testing for ETL workflows and data models.
• Monitor data pipelines for failures, performance bottlenecks, and data drift.
• Implement alerting mechanisms for data quality issues.

Collaboration

• Work closely with the Senior ETL Architect to validate pipeline logic and transformations.
• Partner with business analysts to ensure data meets reporting and analytics requirements.
• Provide feedback to developers on data quality improvements and bug fixes.

Documentation & Standards

• Maintain QA documentation, including test plans, test cases, and defect logs.
• Contribute to establishing data quality standards and best practices.
• Support compliance with data governance, security, and privacy policies.

Required Skills & Qualifications

• Strong experience in data engineering with a focus on QA and testing.
• Proficiency in SQL for data validation and reconciliation.
• Hands-on experience with ETL tools (e.g., Informatica, Talend, or similar).
• Familiarity with big data frameworks (Apache Spark, Databricks) and MPP platforms (Greenplum, Netezza, etc.).
• Knowledge of QA methodologies (unit testing, regression testing, automation frameworks).
• 4 6 years of experience in data engineering, with at least 2 years in QA/testing roles.
• Bachelor s degree in Computer Science, Data Engineering, or related field.
• Familiarity with CI/CD pipelines, version control (Git), and infrastructure-as-code (Terraform, CloudFormation).