Data QA Engineer

THE DEPARTMENT
The Technology department identifies and develops technology strategies that support GVI business goals and objectives, helping ensure internal and external partners are supported by stable, secure, and effective systems. The Data & Analytics function supports trusted data, data products, integrations, reporting, governance, and insights across the organization.

THE POSITION
The Data Quality Assurance Engineer is responsible for planning, designing, and executing automated and manual testing to ensure the accuracy, completeness, reliability, security, and performance of enterprise data solutions. This role works closely with Data Engineers, Analytics Engineers, Product Owners, Business Analysts, Developers, and business stakeholders to validate data pipelines, integrations, transformations, models, reports, and data products against business and technical requirements before release to production.

The Data QA Engineer plays a critical role in ensuring that data is fit for purpose, traceable from source to consumption, and aligned with approved quality standards and business rules. The Data QA Engineer is responsible for the following results:

Test Plan Development
Develop, coordinate, and execute comprehensive test plans for data platforms, pipelines, integrations, migrations, and data products across environments.
Create detailed test cases, scenarios, and test data to validate data completeness, accuracy, consistency, timeliness, uniqueness, and referential integrity.
Design and maintain automated, reusable test scripts for data pipelines, application programming interfaces, batch processes, and system integrations.
Validate source-to-target mappings, transformation logic, business rules, schemas, metadata, and data models.
Perform reconciliation testing across source systems, integration layers, master data platforms, data warehouses, reporting platforms, and downstream applications.
Collaborate with business and technical stakeholders to ensure test coverage aligns with functional requirements, acceptance criteria, and approved data definitions.
Document quality assurance activities, test results, evidence, defects, and exceptions to support audits, compliance, release readiness, and traceability.
Establish repeatable testing protocols for data releases, migrations, upgrades, and third-party integrations.

Delivery
Identify, log, analyze, and track defects related to data quality, transformation logic, integration behavior, security, lineage, and reporting using approved tracking tools.
Perform functional, regression, integration, migration, performance, and data validation testing across data environments.
Validate data movement and synchronization between upstream and downstream systems, including customer, finance, marketing, operational, and reporting platforms.
Confirm exception handling, error logging, restart and recovery behavior, and monitoring controls perform as designed.
Ensure data solutions meet agreed quality, performance, security, privacy, and usability standards.
Continuously improve data QA processes, automation coverage, test data management, controls, and testing tools.
Support user acceptance testing and partner with business stakeholders to validate data products and reporting before production deployment.
Assist with documentation for data rules, mappings, testing procedures, known limitations, and user guidance.

Relationships
Build strong, trust-based relationships with data teams, developers, product teams, governance partners, and business stakeholders.
Act as a quality advocate for enterprise data solutions and promote early testing throughout delivery.
Collaborate with data owners and users to understand real-world data use and anticipate risk areas.
Communicate clearly on defects, data quality findings, test outcomes, risks, dependencies, and release readiness.
Promote a data-driven and customer-focused mindset in solution design and delivery.
Contribute actively to a collaborative, solution-oriented QA team.
Cross-train and provide backup support for other QA team members as needed.

EDUCATION
The preferred candidate will hold a bachelor's degree in Computer Science, Information Systems, Data Analytics, Data Engineering, or a related field; or three to five years of related experience and/or training; or an equivalent combination of education and experience.

EXPERIENCE PREFERRED
3+ years of quality assurance experience, with experience focused on enterprise data platforms, data integration, analytics, or data migration.
Experience testing with Microsoft Dynamics application and/or data
Experience testing data pipelines, extract-transform-load processes, data warehouses, data lakes, master data platforms, or comparable enterprise data solutions.
Experience validating source-to-target mappings, transformation rules, dimensional or relational models, reports, dashboards, and data security controls.
Hands-on experience with Structured Query Language, data profiling, reconciliation, and root-cause analysis.
Experience testing application programming interfaces, file-based integrations, batch processes, and near-real-time data flows.
Experience with test automation tools or frameworks used for data validation and regression testing.
Experience with scripting or programming languages such as Python, Java, or JavaScript.
Database experience with Oracle, SQL Server, or similar relational databases.
Familiarity with cloud data platforms, version control, continuous integration and delivery, and DevOps practices in a QA or release environment.
Ability to translate business data rules into clear, repeatable test scenarios and acceptance criteria.