Senior Data Systems Analyst

About this role

Location(s) Alpharetta, Georgia, Chicago, Illinois, Cincinnati, Ohio, Dallas, Texas, Des Moines, Iowa, Jacksonville, Florida, P&C-Butterfield Road-Downers Grove-IL-AAC

Details

Kemper 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.  

Position Summary:

Kemper is seeking a highly analytical and detail-oriented Data Systems Analyst to provide independent validation and quality assurance across enterprise data platforms, business processes, and reporting solutions. This role is responsible for ensuring the accuracy, completeness, reliability, and regulatory compliance of critical business data and end-to-end data workflows. The Data Systems Analyst serves as an independent quality function within the Data Engineering organization, partnering closely with business stakeholders, data engineers, data architects, product owners, compliance teams, and operational teams to validate business requirements, identify data quality risks, and ensure enterprise data solutions meet business and regulatory expectations. The ideal candidate possesses strong expertise in data analysis, data warehousing, systems analysis, business process validation, testing methodologies, and data governance. This individual will independently assess data quality across source systems, transformations, integrations, reporting platforms, and downstream consumers while driving continuous improvement in enterprise data quality practices.

Position Responsibilities:

Production Incident and Problem Management • Investigate production data incidents and quality issues. • Perform root cause analysis and identify corrective and preventive actions. • Partner with engineering and operational teams to prioritize remediation activities. • Track recurring issues and recommend long-term quality improvements.

Test Strategy and Quality Assurance • Develop and maintain comprehensive testing strategies for enterprise data platforms and business-critical processes. • Create test cases, test scenarios, traceability matrices, and validation documentation. • Establish risk-based testing approaches to ensure appropriate coverage of critical business functions. • Define quality gates and acceptance criteria for data products and platform releases.

Test Automation and Quality Frameworks • Collaborate with data engineering teams to develop reusable testing assets and automated validation processes. • Support implementation of automated testing frameworks for data validation, reconciliation, regression testing, and quality monitoring. • Promote quality engineering best practices across the data organization.

Regression and Release Validation • Conduct regression testing across enterprise systems following enhancements, migrations, platform upgrades, and releases. • Assess downstream impacts of system and data changes. • Validate production deployments and release readiness.

Non-Functional Testing • Support performance, scalability, reliability, recoverability, and operational readiness testing. • Validate system behavior under expected and peak business workloads. • Assess data processing performance and service-level requirements.

End-to-End Data Workflow Testing • Design and execute test plans for complex business and data workflows spanning multiple applications, databases, integrations, and reporting platforms. • Validate data movement across source systems, ETL/ELT processes, data warehouses, reporting environments, and downstream consumers. • Perform system integration testing, user acceptance testing support, and production validation activities.

Business Requirements Analysis • Partner with business stakeholders, product owners, and data engineering teams to clarify and refine requirements. • Translate business requirements into testable scenarios and validation criteria. • Challenge assumptions and identify requirement gaps, ambiguities, and potential quality risks early in the delivery lifecycle.

Data Quality Governance and Metrics • Develop and monitor data quality KPIs, controls, and scorecards. • Support enterprise data quality governance initiatives. • Contribute to the establishment of data quality standards, policies, and operating procedures. • Drive continuous improvement of data quality management practices.

Independent Business Process Validation • Independently validate critical business processes and supporting data workflows across operational, analytical, and regulatory systems. • Evaluate end-to-end business process execution to ensure data integrity, accuracy, completeness, and consistency throughout the data lifecycle. • Identify control gaps, data risks, process deficiencies, and opportunities for quality improvement.

Data Quality Analysis and Validation • Perform independent validation of enterprise data assets, reports, dashboards, and regulatory submissions. • Conduct data profiling, reconciliation, root cause analysis, and quality assessments across structured and semi-structured data. • Validate business rules, transformations, calculations, aggregations, and reporting logic. • Analyze data anomalies, trends, and quality metrics to identify potential issues and risks.

Governance, Compliance, and Regulatory Validation • Ensure compliance with enterprise data governance standards, policies, and controls. • Validate regulatory, audit, financial, operational, and compliance-related data requirements. • Support internal and external audit activities through in

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