{"id":1523880,"url":"https://alion.io/job/kemper-data-quality-engineer","title":"Data Quality Engineer","company":{"id":1805827,"name":"Kemper","domain":"kemper.com","url":"https://alion.io/company/kemper-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-01T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chicago, United States","Jacksonville, United States","Alpharetta, United States","Downers Grove, United States","Birmingham, United States","United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":99000,"max":164800,"currency":"USD","period":"year","gross":null,"usd_annual":164800},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Informatica","optional":false},{"name":"Oracle","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Anomaly Detection","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Data Vault","optional":true},{"name":"Git","optional":true},{"name":"Machine Learning","optional":true},{"name":"PowerShell","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-07-10T00:00:00Z","employer_posted_date":"2026-07-10","last_verified_at":"2026-10-01T10:41:08Z","board_verified":true,"closed_at":null,"days_open":83,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":83},"description":"Location(s)\nAlpharetta, Georgia, Birmingham, Alabama, Chicago, Illinois, Downers Grove, Illinois, Jacksonville, Florida, Remote-CT, Remote-NJ, Remote-OH, Remote-PA, Remote-RI, Remote-VADetails\nKemper is one of the nation’s leading specialized insurers. Our success is a direct reflection of the talented and diverse people who make a positive difference in the lives of our customers every day. We believe a high-performing culture, valuable opportunities for personal development and professional challenge, and a healthy work-life balance can be highly motivating and productive. Kemper’s products and services are making a real difference to our customers, who have unique and evolving needs. By joining our team, you are helping to provide an experience to our stakeholders that delivers on our promises.\nPOSITION SUMMARY:\nKemper 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.\nThe 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.\nAs 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.\nPosition Responsibilities:\nDesign and Develop Data Testing Solutions\nBuild, 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.\nData Validation and Quality Assurance\nDevelop and execute data validation routines for extracts, transformations, and reporting datasets to ensure completeness, accuracy, consistency, and reliability of enterprise data assets.\nTest Automation and Reconciliation\nDesign automated reconciliation processes between source and target systems, including row count validation, schema validation, transformation testing, and data profiling.\nData Pipeline Quality Engineering\nPartner 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.\nAI-Enabled Test Development and Automation\nLeverage 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.\nTest Environment Strategy and Management\nSupport 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.\nData Governance and Compliance\nEnsure compliance with enterprise data governance, security, and regulatory requirements by implementing data quality standards, monitoring controls, and audit-ready validation processes.\n Integration and Monitoring\nWork with structured and semi-structured data formats (XML, JSON) and cloud-native services to validate data ingestion, transformation, and integration processes across distributed platforms.\nCollaboration and Leadership\nCollaborate 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.\nContinuous Improvement\nRecommend 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.\nPosition Qualifications:\nRequired Skills and Experience\nBachelor’s degree in Computer Science, Information Systems, or a related field; equivalent work experience considered.\n6+ years of experience in data engineering, data testing, or database development.\nDemonstrated expertise in:SQL development and query tuning\nAutomated data testing and validation methodologies\nInformatica and IICS for ETL and data integration testing\nSnowflake data warehouse architecture and validation\nOracle database systems\nData reconciliation and data profiling techniques\nData modeling, normalization, and relational design\nHandling and validating XML and JSON data structures\nBuilding data quality solutions in AWS cloud environments\nPython-based automation and testing frameworks\n\nStrong 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.\nExperience establishing and supporting end-to-end test strategies for enterprise data pipelines and distributed data platforms.\nUnderstanding of environment dependencies, release validation processes, and data synchronization considerations for large-scale data ecosystems.\nExperience developing automated test scripts and reusable validation frameworks.\nStrong understanding of ETL/ELT testing methodologies and end-to-end data flow validation.\nStrong problem-solving abilities and the capacity to work independently on complex technical challenges.\nDeep understanding of data security, governance, compliance, and data quality best practices.\nHigh degree of self-motivation, intellectual curiosity, and commitment to continuous improvement.\nPreferred Qualifications\nInsurance industry experience (P&C and/or Life).\nExperience working with IDMC/IICS.\nExperience with Data Vault 2.0 methodologies.\nExperience with data quality and observability tools.\nExperience with PowerShell or Python for automation and scripting.\nKnowledge of Git and CI/CD pipelines for automated testing and deployment.\nExposure to hybrid or multi-cloud data architectures.\nExperience with Spark, Kafka, Airflow, DBT, and Infrastructure as Code frameworks.\nExperience implementing automated monitoring, alerting, and anomaly detection for data pipelines.\nFamiliarity with DevOps and DataOps practices for enterprise data platforms.\nExperience supporting Power BI reporting and downstream analytics validation.\nExperience utilizing AI-assisted development and testing tools to accelerate test case generation, validation scripting, anomaly detection, and quality engineering processes.\nFamiliarity with AI-enabled data observability, intelligent test automation, and machine learning-assisted quality monitoring solutions.\nExperience leveraging generative AI tools for SQL validation, automated documentation, test optimization, and pipeline quality analysis.\nThe position can be worked hybrid out of a local Kemper office or remotely for a non-local candidate.\nSponsorship is not accepted for this position.\nThe range for this position is $99,000 to $164,800. When determining candidate offers, we consider experience, skills, education, certifications, and geographic location among other factors. This job is eligible for an annual discretionary bonus and Kemper benefits (Medical, Dental, Vision, PTO, 401k, etc.)\nKemper is proud to be an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, disability status or any other status protected by the laws or regulations in the locations where we operate. We are committed to supporting diversity and equality across our organization and we work diligently to maintain a workplace free from discrimination.\nKemper does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Kemper and Kemper will not be obligated to pay a placement fee.\nKemper will never request personal information, such as your social security number or banking information, via text or email. Additionally, Kemper does not use external messaging applications like WireApp or Skype to communicate with candidates. 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