Senior Data Systems Analyst

Data Systems Analyst

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:

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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 independent quality assessments and evidence collection.
• Verify adherence to data lineage, data retention, privacy, and security requirements.

• Serve as a trusted advisor on data quality and validation practices.
• Collaborate across business, technology, risk, compliance, and operational teams.
• Mentor junior analysts and promote quality-focused thinking across the organization.
• Champion a culture of quality, accountability, and continuous improvement.

Position Qualifications:

Required Skills and Experience:

• Bachelor's degree in Information Systems, Computer Science, Data Analytics, Business Analytics, or a related field; equivalent work experience considered.
• 6+ years of experience in one or more of the following areas:
• Data Quality Analysis
• Data Warehousing
• Business Systems Analysis
• Data Governance
• Quality Assurance
• Data Testing
• Business Process Validation
• Insurance industry experience (P&C and/or Life Insurance).
• Experience supporting enterprise data warehouse environments.

Demonstrated Expertise In:

• Data quality management principles and methodologies
• End-to-end business process testing
• Data warehouse validation and reporting verification
• Data reconciliation and data profiling techniques
• SQL querying and data analysis
• Root cause analysis and problem-solving methodologies
• Test planning, test design, and test execution
• Regression testing and release validation
• Requirements analysis and requirements traceability
• Data governance and data stewardship practices
• Regulatory, compliance, and audit-related data validation
• Production incident investigation and resolution support
• Data lineage, metadata, and data quality controls
• Relational database concepts and data modeling
• Validation of ETL/ELT processes and enterprise data pipelines

Technical Skills:

• Advanced SQL development and data analysis
• Experience working with Snowflake, Oracle, SQL Server, or similar database platforms
• Familiarity with Informatica, IICS, or enterprise data integration platforms
• Experience using reporting and analytics tools such as Power BI
• Experience working with XML, JSON, and API-based integrations
• Familiarity with Python or other scripting languages for data analysis and automation
• Experience with Azure, AWS, or cloud-based data platforms

Professional Competencies:

• Strong analytical and critical thinking skills
• Excellent communication and stakeholder management abilities
• Ability to work independently with minimal supervision
• Strong documentation and organizational skills
• High attention to detail and commitment to data accuracy
• Ability to manage multiple priorities in a fast-paced environment
• Strong intellectual curiosity and continuous improvement mindset

Preferred Qualifications:

• Experience with data quality, observability, or governance tools.
• Familiarity with CI/CD practices and automated testing frameworks.
• Experience with DataOps, DevOps, or Agile delivery methodologies.
• Exposure to large-scale cloud data platforms and distributed data ecosystems.
• Experience with AI-assisted testing, validation, and data quality monitoring tools.
• Knowledge of data lineage, metadata management, and master data management concepts.
• Experience supporting enterprise audit and regulatory compliance initiatives.
• The position can be worked hybrid out of a local Kemper office or remotely for a non-local candidate.