Data Qa Engineer

Role Data QA Engineer

Type: Contract for 6 months to 1 year

Remote

Exp: 3-6 years

We are seeking an experienced Data QA Engineer to join our team and ensure the

accuracy, consistency, and reliability of our data products and pipelines. You will play a key

role in validating data at scale, implementing automated quality checks, and collaborating

closely with engineering and product teams. The ideal candidate has a strong foundation in

testing methodologies, data validation techniques, and test case management, with hands-

on expertise in Python, automation, and AWS. Experience with big data technologies

like Pyspark, Big Query, and data analysis libraries such as Pandas is a strong advantage.

Key Responsibilities

 Design, develop, and execute data validation test plans and test cases for data

pipelines, ETL processes, and data-driven applications.

 Build and maintain automated data quality checks and regression test

suites using Python.

 Identify, document, and track data issues through to resolution.

 Validate data transformations, aggregations, and reporting outputs against source

data and business rules.

 Collaborate with data engineers, analysts, and product managers to define effective

test strategies and clear acceptance criteria.

 Monitor, validate, and troubleshoot data pipelines deployed on AWS cloud

infrastructure.

 Analyze large datasets to detect anomalies and ensure data integrity.

 Continuously improve QA processes, tools, and best practices for data-centric

testing.

Required Skills & Qualifications

 Minimum 3-6 years of experience in QA/testing, with a focus on data

validation and data-centric testing.

 Strong knowledge of testing methodologies, defect tracking, and test case

management tools.

 Proficiency in Python, with experience automating test cases and developing

validation scripts.

 Hands-on experience with AWS services such as S3, EMR, SQS, Athena, etc.

 Solid understanding of SQL and ability to write and optimize queries for data

validation.

 Excellent analytical and problem-solving skills, with keen attention to detail.

 Strong communication skills and ability to thrive in a collaborative, cross-functional

team environment.

Preferred Skills (Good-to-Have)

 Experience with big data tools: PySpark, BigQuery, etc.

 Familiarity with distributed data processing frameworks and architectures.

 Knowledge of data analysis libraries such as Pandas, NumPy, etc.

 Exposure to CI/CD pipelines and QA automation frameworks.