CF - 1473 - QA Analyst - Data Analytics

Junior QA Analyst – Data & Analytics

Role Summary

We are seeking a Junior QA Analyst to support testing and quality assurance activities across data engineering, data science, analytics, and integration projects. The role will focus on validating data pipelines, ETL processes, data quality, reports, dashboards, and business rules to ensure accurate and reliable data is delivered to stakeholders.

Key Responsibilities

• Execute test cases for data pipelines, ETL processes, APIs, and reporting solutions.
• Validate source-to-target data mappings and perform data reconciliation checks.
• Write and execute SQL queries to verify data accuracy, completeness, and consistency.
• Perform data quality testing, including row count validation, duplicate checks, null checks, referential integrity, and business rule validation.
• Validate dashboards, reports, and analytical outputs against business requirements.
• Work closely with Data Engineers, Data Scientists, Business Analysts, and stakeholders to understand data flows and expected outcomes.
• Investigate and document defects, data issues, and root causes.
• Assist in the creation and maintenance of test plans, test cases, and QA documentation.
• Participate in UAT, regression testing, and release validation activities.
• Support continuous improvement of QA processes and test automation initiatives.
  
  

Required Skills & Experience

• Basic understanding of SQL and relational databases.
• Ability to read, analyze, and validate data from multiple sources.
• Understanding of ETL/ELT concepts, data pipelines, and data transformations.
• Knowledge of data quality concepts such as completeness, accuracy, consistency, and integrity.
• Ability to understand business requirements and validate how data is used within reports, dashboards, and operational processes.
• Strong analytical and problem-solving skills.
• Good communication and documentation skills.
• Familiarity with Excel and data analysis techniques.
  
  

Desirable Skills

• Exposure to AWS, Azure, or cloud-based data platforms.
• Knowledge of reporting tools such as Power BI, QuickSight, Tableau, or GoodData.
• Understanding of data science workflows, datasets, model inputs/outputs, and feature validation.
• Experience with Jira, Azure DevOps, or similar testing and defect management tools.
• Exposure to Python for basic data validation or testing activities.
  
  

Ideal Candidate

A detail-oriented graduate or early-career professional who is passionate about data, enjoys investigating problems, understands how businesses use data for decision-making, and wants to build a career in Data Quality, Data Engineering, Analytics, or Data Science QA.

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