Remote Data Quality Engineer: AI‑Driven Validation

Kemper

Richmond (VA)

Hybrid

USD 99,000 - 164,800

Full time

14 days+

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Benefits offered by this job

Health and dental plans

401(k) match

Tuition assistance

Paid time off

Employee discounts

Job summary

Kemper 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.

The ideal candidate will lead automated testing, validation, and CI/CD integration across Snowflake, Oracle, and AWS, while mentoring teammates and ensuring governance, security, and compliance across data platforms.

Qualifications

Bachelor’s degree in Computer Science, Information Systems, or a related field.
6+ years of experience in data engineering, data testing, or database development.
SQL development and query tuning.
Automated data testing and validation methodologies.
Informatica and IICS for ETL and data integration testing.
Snowflake data warehouse architecture and validation.
Oracle database systems.
Data reconciliation and data profiling techniques.
Data modeling, normalization, and relational design.
Handling and validating XML and JSON data structures.
Building data quality solutions in AWS cloud environments.
Python‑based automation and testing frameworks.

Responsibilities

Build, 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.
Develop and execute data validation routines for extracts, transformations, and reporting datasets to ensure completeness, accuracy, consistency, and reliability of enterprise data assets.
Design automated reconciliation processes between source and target systems, including row count validation, schema validation, transformation testing, and data profiling.
Partner 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.
Leverage 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.
Support 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.
Ensure compliance with enterprise data governance, security, and regulatory requirements by implementing data quality standards, monitoring controls, and audit‑ready validation processes.
Work with structured and semi-structured data formats (XML, JSON) and cloud‑native services to validate data ingestion, transformation, and integration processes across distributed platforms.
Collaborate 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.
Recommend 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.