Data Business Systems Analyst

Kemper•Chicago, IL

2d•Hybrid

About The Position

Kemper is seeking a highly analytical and detail-oriented Senior Data Business Systems Analyst to provide independent validation and quality assurance across enterprise data platforms, business processes, and reporting solutions. The role ensures the accuracy, completeness, reliability, and regulatory compliance of business data and end-to-end data workflows. It serves as an independent quality function within the Data Engineering organization, partnering with business stakeholders, data engineers, data architects, product owners, compliance teams, and operational teams. The analyst independently assesses data quality across source systems, transformations, integrations, reporting platforms, and downstream consumers, while supporting continuous improvement in enterprise data quality practices. The position may be worked hybrid from a local Kemper office or remotely for a non-local candidate. The stated salary range is $89,000 to $148,100, and the role is eligible for an annual discretionary bonus and Kemper benefits.

Requirements

Bachelor's degree in Information Systems, Computer Science, Data Analytics, Business Analytics, or a related field; equivalent work experience is considered.
At least 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, or business process validation.
Insurance industry experience in property and casualty and/or life insurance.
Experience supporting enterprise data warehouse environments.
Expertise in data quality management principles and methodologies.
Expertise in end-to-end business process testing, data warehouse validation, and reporting verification.
Experience with data reconciliation, data profiling, SQL querying, and data analysis.
Experience with root cause analysis and problem-solving methodologies.
Experience in test planning, test design, test execution, regression testing, and release validation.
Experience in requirements analysis and requirements traceability.
Knowledge of data governance and data stewardship practices.
Experience with regulatory, compliance, and audit-related data validation and production incident investigation and resolution support.
Knowledge of data lineage, metadata, data quality controls, relational database concepts, and data modeling.
Experience validating ETL/ELT processes and enterprise data pipelines.
Advanced SQL development and data analysis skills.
Experience 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.
Strong analytical and critical thinking skills, excellent communication and stakeholder management abilities, independence, strong documentation and organizational skills, attention to detail, ability to manage multiple priorities, and a continuous improvement mindset.

Nice To Haves

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.

Responsibilities

Investigate production data incidents and quality issues, perform root cause analysis, identify corrective and preventive actions, and work with engineering and operational teams to prioritize remediation.
Track recurring data issues and recommend long-term quality improvements.
Develop and maintain testing strategies, test cases, scenarios, traceability matrices, and validation documentation for enterprise data platforms and critical business processes.
Establish risk-based testing approaches, 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 for data validation, reconciliation, regression testing, and quality monitoring.
Promote quality engineering best practices across the data organization.
Conduct regression testing after enhancements, migrations, platform upgrades, and releases; assess downstream impacts and validate production deployments and release readiness.
Support performance, scalability, reliability, recoverability, and operational-readiness testing, including validation under expected and peak workloads.
Design and execute test plans for workflows spanning applications, databases, integrations, reporting platforms, source systems, ETL/ELT processes, data warehouses, and downstream consumers.
Perform system integration testing, support user acceptance testing, and conduct production validation.
Partner with business stakeholders, product owners, and data engineering teams to clarify and refine requirements; translate requirements into testable scenarios and validation criteria.
Identify requirement gaps, ambiguities, assumptions, and potential quality risks early in the delivery lifecycle.
Develop and monitor data quality KPIs, controls, and scorecards; support data quality governance initiatives and contribute to standards, policies, and operating procedures.
Independently validate critical business processes and supporting data workflows across operational, analytical, and regulatory systems.
Evaluate end-to-end process execution for data integrity, accuracy, completeness, and consistency; identify control gaps, data risks, process deficiencies, and improvement opportunities.
Validate enterprise data assets, reports, dashboards, and regulatory submissions through profiling, reconciliation, root cause analysis, and quality assessments of structured and semi-structured data.
Validate business rules, transformations, calculations, aggregations, and reporting logic; analyze anomalies, trends, and quality metrics to identify issues and risks.
Ensure adherence to enterprise data governance standards, policies, controls, regulatory requirements, data lineage, data retention, privacy, and security requirements.
Support internal and external audits through independent quality assessments and evidence collection.
Advise on data quality and validation practices; collaborate across business, technology, risk, compliance, and operational teams.
Mentor junior analysts and promote quality, accountability, and continuous improvement.