Data Business Systems Analyst

Kemper•St. Louis, MO

3d•$89,000 - $148,100•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 critical business data and end-to-end data workflows. The analyst 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 position validates business requirements, identifies data quality risks, and ensures enterprise data solutions meet business and regulatory expectations. It independently assesses data quality across source systems, transformations, integrations, reporting platforms, and downstream consumers while supporting continuous improvement of enterprise data quality practices. The position may be worked hybrid from a local Kemper office or remotely for a non-local candidate. Listed office locations are Charlotte, North Carolina; Chicago, Illinois; Columbus, Ohio; Dallas, Texas; Denver, Colorado; and St. Louis, Missouri.

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: data quality analysis, data warehousing, business systems analysis, data governance, quality assurance, data testing, or business process validation.
Insurance industry experience in Property & Casualty and/or Life Insurance.
Experience supporting enterprise data warehouse environments.
Expertise in data quality management principles and methodologies, end-to-end business process testing, data warehouse validation, reporting verification, data reconciliation, and data profiling.
Advanced SQL development and data analysis skills.
Expertise in root cause analysis, test planning, test design and execution, regression testing, release validation, requirements analysis, and requirements traceability.
Knowledge of data governance, data stewardship, regulatory and audit-related data validation, production incident investigation, data lineage, metadata, and data quality controls.
Knowledge of relational database concepts, data modeling, and validation of ETL/ELT processes and enterprise data pipelines.
Experience with Snowflake, Oracle, SQL Server, or similar database platforms.
Strong analytical and critical thinking skills, communication and stakeholder management abilities, documentation and organizational skills, and attention to detail.
Ability to work independently with minimal supervision, manage multiple priorities in a fast-paced environment, and maintain 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.
Familiarity with Informatica, IICS, or enterprise data integration platforms.
Experience with 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.

Responsibilities

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