Data Quality Engineer

Kemper is one of the nation’s leading specialized insurers, dedicated to providing exceptional services to their customers. They are seeking a Data Quality Engineer to design, implement, and optimize data validation frameworks ensuring the accuracy and integrity of business-critical data solutions. This role will involve technical leadership in data testing, validation, and quality assurance processes across various data platforms.

Responsibilities:

• 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
• Develop and execute data validation routines for extracts, transformations, and reporting datasets to ensure completeness, accuracy, consistency, and reliability of enterprise data assets
• Design automated reconciliation processes between source and target systems, including row count validation, schema validation, transformation testing, and data profiling
• 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
• 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
• 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
• Ensure compliance with enterprise data governance, security, and regulatory requirements by implementing data quality standards, monitoring controls, and audit-ready validation processes
• 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
• 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
• 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:

• 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
• Demonstrated expertise in automated data testing and validation methodologies
• Demonstrated expertise in Informatica and IICS for ETL and data integration testing
• Demonstrated expertise in Snowflake data warehouse architecture and validation
• Demonstrated expertise in Oracle database systems
• Demonstrated expertise in data reconciliation and data profiling techniques
• Demonstrated expertise in data modeling, normalization, and relational design
• Demonstrated expertise in handling and validating XML and JSON data structures
• Demonstrated expertise in building data quality solutions in AWS cloud environments
• Demonstrated expertise in 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
• Insurance industry experience (P&C and/or Life)
• Experience working with IDMC/IICS
• Experience with Data Vault 2.0 methodologies
• Experience with data quality and observability tools
• Experience with PowerShell or Python for automation and scripting
• Knowledge of Git and CI/CD pipelines for automated testing and deployment
• Exposure to hybrid or multi-cloud data architectures
• Experience with Spark, Kafka, Airflow, DBT, and Infrastructure as Code frameworks
• Experience implementing automated monitoring, alerting, and anomaly detection for data pipelines
• Familiarity with DevOps and DataOps practices for enterprise data platforms
• Experience supporting Power BI reporting and downstream analytics validation
• Experience utilizing AI-assisted development and testing tools to accelerate test case generation, validation scripting, anomaly detection, and quality engineering processes
• Familiarity with AI-enabled data observability, intelligent test automation, and machine learning-assisted quality monitoring solutions
• Experience leveraging generative AI tools for SQL validation, automated documentation, test optimization, and pipeline quality analysis