Data Quality Engineer

Kemper•Alpharetta, CT

1d•$99,000 - $164,800•Hybrid

About The Position

Kemper is seeking a Data Quality Engineer specializing in Data Testing and Quality Engineering to design, implement, and optimize enterprise data validation frameworks that ensure the accuracy, reliability, and integrity of business-critical data solutions. This role provides technical leadership across data testing, validation, reconciliation, automation, and quality assurance processes supporting analytics, reporting, and operational systems. The ideal candidate is a self-motivated problem solver with strong intellectual curiosity, deep expertise in data engineering and automated testing practices, and a strong understanding of data governance, security, and compliance principles. As a senior member of the data engineering team, you will be responsible for developing scalable data validation frameworks, ensuring data integrity across pipelines and platforms, implementing automated testing strategies throughout the data lifecycle, and supporting enterprise test environment strategy across complex data ecosystems.

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
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.

Nice To Haves

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.

Responsibilities

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.