Data Quality Engineer

Kemper

Data Quality Engineer specializing in enterprise data validation frameworks and quality assurance. At Kemper, contribute to improving data integrity and enhancing analytics capabilities.

Posted 7/8/2026full-timeAlpharetta • Alabama, Connecticut, Florida, Illinois, New Jersey, Pennsylvania, Rhode Island, Virginia • 🇺🇸 United StatesMid-LevelSenior💰 $99,000 - $164,800 per yearWebsite

Tech Stack

Tools & technologies

AWSCloudETLInformaticaOraclePythonSQL

About the role

Key responsibilities & impact

Design and Develop Data Testing Solutions
Build, maintain, and optimize automated data testing frameworks and validation pipelines that support enterprise reporting, analytics, and business applications using SQL, Informatica, IICS, Snowflake, and Python.
Data Validation and Quality Assurance
Develop and execute data validation routines for extracts, transformations, and reporting datasets to ensure completeness, accuracy, consistency, and reliability of enterprise data assets.
Test Automation and Reconciliation
Design automated reconciliation processes between source and target systems, including row count validation, schema validation, transformation testing, and data profiling.
Data Pipeline Quality Engineering
Partner with data engineering teams to embed testing and quality controls into ETL/ELT pipelines and CI/CD deployment processes across Snowflake, Oracle, and AWS environments.
AI-Enabled Test Development and Automation
Leverage AI-assisted development tools and intelligent automation techniques to improve test coverage, accelerate validation processes, and enhance the efficiency of data quality engineering practices across enterprise data platforms.
Test Environment Strategy and Management
Support and contribute to enterprise test environment strategy, including environment planning, test data management, deployment coordination, integration testing support, and validation across development, QA, UAT, and production environments.
Data Governance and Compliance
Ensure compliance with enterprise data governance, security, and regulatory requirements by implementing data quality standards, monitoring controls, and audit-ready validation processes.
Integration and Monitoring
Work with structured and semi-structured data formats (XML, JSON) and cloud-native services to validate data ingestion, transformation, and integration processes across distributed platforms.
Collaboration and Leadership
Collaborate with data engineers, analysts, QA teams, and business stakeholders to define testing requirements, improve data quality processes, and support reporting solutions such as Power BI.
Continuous Improvement
Recommend and implement improvements to data quality frameworks, testing automation, monitoring solutions, governance processes, and DataOps practices.
Mentor junior team members and promote best practices in data quality engineering and testing.

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Information Systems, or a related field; equivalent work experience considered.
6+ years of experience in data engineering, data testing, or database development.
Demonstrated expertise in: SQL development and query tuning
Automated data testing and validation methodologies
Informatica and IICS for ETL and data integration testing
Snowflake data warehouse architecture and validation
Oracle database systems
Data reconciliation and data profiling techniques
Data modeling, normalization, and relational design
Handling and validating XML and JSON data structures
Building data quality solutions in AWS cloud environments
Python-based automation and testing frameworks
Strong knowledge of test environment strategy, including environment planning, test data management, deployment coordination, integration testing support, and validation across development, QA, UAT, and production environments.
Experience establishing and supporting end-to-end test strategies for enterprise data pipelines and distributed data platforms.
Understanding of environment dependencies, release validation processes, and data synchronization considerations for large-scale data ecosystems.
Experience developing automated test scripts and reusable validation frameworks.
Strong understanding of ETL/ELT testing methodologies and end-to-end data flow validation.
Strong problem-solving abilities and the capacity to work independently on complex technical challenges.
Deep understanding of data security, governance, compliance, and data quality best practices.
High degree of self-motivation, intellectual curiosity, and commitment to continuous improvement.

Benefits

Comp & perks

Health insurance
Dental insurance
Vision insurance
Paid time off
401(k)
Annual discretionary bonus
Tuition assistance program
Paid certifications and continuing education programs
Employee discounts through Kemper Perks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

SQL DevelopmentAutomated Data TestingData ValidationData ReconciliationData ProfilingData ModelingPython AutomationETL/ELT Testing MethodologiesData Quality SolutionsTest Environment Strategy

Soft Skills

Problem-SolvingCollaborationMentoringContinuous ImprovementSelf-Motivation