Data Quality Engineer

symplr®; Bengaluru; On-site; INR 1,200,000 - 1,700,000; Full time

Job summary

symplr is seeking a Senior Data Quality Engineer with 7+ years of experience to advance testing initiatives for our data platform. You will ensure integrity, reliability, and performance of data workflows across AWS services, ETL processes, and Power BI reporting.

The role requires hands-on ETL testing, data validation, Python scripting, and collaboration with data engineers and business stakeholders in an Agile environment.

Qualifications

• 7+ years experience in data quality engineering or related field.
• Hands-on ETL testing and data validation experience.
• Experience with AWS data services (Glue, S3, Athena, MSK, SQS, Lambda, Step Functions).
• Proficiency in SQL and Python (or Java/C#/C++).
• Experience validating data in Power BI reports.
• Familiarity with Azure DevOps, Zephyr or equivalent.
• Experience in Agile environments (Scrum/Kanban).
• Good communication and cross-functional collaboration.

Responsibilities

• Design and execute data quality strategies and tests.
• Develop tests for ETL pipelines and end-to-end data flows.
• Monitor and optimize AWS-based data pipelines.
• Develop Python scripts for data quality checks.
• Validate data lineage and transformations in Power BI reports.
• Document testing procedures and quality metrics.
• Collaborate with data engineers, analysts, and stakeholders.

Skills

ETL testing

Data validation

SQL

Python

Power BI

Azure DevOps

Zephyr

Agile (Scrum/Kanban)

Data governance

Data observability

Communication

Tools

AWS Glue

AWS S3

AWS Athena

AWS MSK

AWS SQS

AWS Lambda

AWS Step Functions

Job description

Overview

symplr is seeking a highly skilled and motivated Senior Data Quality Engineer with 7+ years of experience to execute testing initiatives for our robust Data Platform. This role is instrumental in maintaining the integrity, reliability, and performance of our data systems. The ideal candidate will bring strong expertise in ETL testing, data validation, AWS-based data workflows, and Power BI reports, with a solid foundation in Test Automation and scripting.

Duties & Responsibilities

• Contribute to the development and execution of data quality strategies and best practices to support the organization’s data initiatives.

• ETL Testing: Design and perform thorough ETL tests, including test case development, data validation, transformation accuracy, end-to-end pipeline testing, and performance testing.

• AWS Data Pipeline Monitoring: Support the monitoring and optimization of AWS-based pipelines using services like Glue, S3, Athena, MSK, SQS, Lambda, Step Functions etc to ensure data accuracy, completeness, and reliability.

• Automation & Scripting: Develop Python-based scripts to automate data quality checks and validation processes.

• Power BI Reports: Ensure data accuracy and consistency in Power BI reports by validating data lineage, transformation logic, and report outputs against source systems.

• Collaboration: Work alongside data engineers, analysts, and business stakeholders to understand data flows and resolve data quality issues.

• Documentation: Maintain clear and detailed documentation of testing procedures, test results, and quality metrics.

Skills Required

• 5+ years of experience in data quality engineering, data testing, or related fields.

• Strong hands-on experience with ETL testing and data validation techniques.

• Experience working with AWS data services, including Glue, S3, Athena, MSK, SQS, Lambda, Step Functions, etc.

• Proficiency in SQL and Python (or any other OOP languages like Java, C#, C++, etc)

• Experience validating data in Power BI reports, including understanding of data models and report logic.

• Familiarity with test management tools such as Azure DevOps, Zephyr, or equivalent.

• Experience working in Agile environments (Scrum/Kanban).

• Exposure to data governance principles or data observability tools is a plus.

• Strong problem-solving skills and a proactive approach to identifying and resolving data quality issues.

• Good communication skills and the ability to work effectively with cross-functional teams.

• Continuous learning mindset and willingness to adapt to new technologies and techniques.