QA Engineer

Job Description Summary

We are hiring a meticulous and skilled QA Engineer with expertise in data validation, report testing, and automation. The ideal candidate will have hands-on experience testing data pipelines, Power BI dashboards, and automated data workflows, ensuring end-to-end data quality and report accuracy.

Key Responsibilities

Design and execute test strategies for ETL pipelines, data transformations, and BI dashboards.

Conduct manual and automated testing for data pipelines and reports.

Validate Power BI dashboards and reports for data correctness, layout integrity, and performance.

Automate data validations using SQL and Python (e.g., with PyTest, dbt tests, or custom scripts).

Perform cross-system data comparisons to ensure consistency across data layers and reports.

Collaborate with data engineers and analysts to understand business logic and ensure test coverage.

Maintain test documentation, including test plans, cases, and defect logs (e.g., in Jira).

Participate in agile sprints, release planning, and code reviews to embed QA early in the cycle.

Qualifications

Bachelor’s degree in information technology or related field; or an equivalent combination of education and experience sufficient to successfully perform the key accountabilities of the job required.

Exposure to Power BI DAX expressions and filters for deeper validation.

Experience with API testing tools (e.g., Postman, REST Assured).

Background in BI regression testing and automated report verification.

Familiarity with cloud platforms like AWS, Azure, or GCP.

Experience

3–6 years

in QA roles, with focus on

data engineering

and

reporting environments .

Proficiency in

SQL

for querying and validating large datasets.

Experience testing

Power BI

reports and dashboards (or similar tools like Tableau / Looker).

Experience with

test automation frameworks

(e.g., PyTest, Selenium, dbt tests).

Familiarity with

ETL tools ,

Airflow , and

data warehouses

like

Snowflake

or

BigQuery .

Understanding

data quality concepts : completeness, accuracy, consistency, freshness.

Experience with version control (Git), CI / CD pipelines, and issue tracking (e.g., Jira).