Quality Engineer - Data Quality & Test Data Management

Role Overview

We are seeking a Quality Engineer with a strong data and technical background to focus on test data management, SQL‑driven validation, and data quality assurance for a cloud‑native healthcare application. This role is essential to ensuring the accuracy, consistency, and integrity of data used for reporting, analytics, integrations, and data migrations/conversions.

This position operates as a core member of an Agile Scrum team, working closely with Software Engineers, Quality Engineers, Quality Analysts and product stakeholders to validate complex data flows end‑to‑end. The ideal candidate combines traditional QA discipline with deep analytical thinking, strong SQL skills, and a clear understanding of how healthcare data quality impacts customers, regulatory outcomes, and business trust.

Key Responsibilities

Test Data Management

• Own and maintain test data strategies that support functional testing, regression testing, performance testing, reporting validation, and data migrations.
• Create, curate, and validate high‑quality test datasets that reflect real‑world healthcare scenarios, edge cases, and historical data patterns.
• Ensure test data remains accurate, reusable, and aligned with evolving schemas and business rules.
• Partner with engineering to identify and mitigate risks related to shared databases and cross‑functional data dependencies.

Data Quality & Validation Testing

• Validate data extractions, transformations, and loads (ETL) used for reporting, downstream feeds, and analytics.
• Test and reconcile data used in:
  • Operational and regulatory reports
  • Customer‑facing reports
  • Internal analytics and dashboards
• Perform data reconciliation testing to ensure completeness, accuracy, and consistency across systems.

Data Migration & Conversion Testing

• Design and execute test plans for data migrations and conversions, validating both historical and in‑flight data.
• Confirm that migrated data preserves:
  • Referential integrity
  • Business rules and calculations
  • Regulatory and healthcare data requirements
• Partner with engineering and stakeholders to validate pre‑ and post‑migration data states.

SQL & Technical Analysis

• Use advanced SQL queries to validate data correctness, detect anomalies, and identify data quality issues.
• Independently analyze data issues and support root‑cause analysis in collaboration with engineering teams.
• Validate schema changes, data model updates, and database‑level impacts as part of sprint delivery.

Agile & Scrum Team Participation

• Act as a full participant in an Agile Scrum team, contributing to sprint planning, backlog refinement, stand‑ups, reviews, and retrospectives.
• Ensure data‑related acceptance criteria are clearly defined and testable before work enters a sprint.
• Identify data quality risks early in the sprint lifecycle and communicate them clearly to the team.

Test Design & Automation Readiness

• Create data‑centric test plans and test cases that are structured, repeatable, and automation‑ready.
• Leverage AI‑assisted tools to help generate test scenarios, identify edge cases, and improve coverage for complex data sets (with appropriate validation and oversight).
• Collaborate with Quality Engineers to transition suitable data tests into automated pipelines.

Defect Management & Continuous Improvement

• Clearly document and communicate data defects, including reproduction queries, expected vs. actual results, and data impact.
• Identify recurring data issues and recommend systemic improvements to prevent future defects.
• Contribute to improvements in data quality standards, metrics, and QA practices.

Required Qualifications

• 4–8 years of experience in software quality assurance with a strong emphasis on data validation and testing.
• Strong SQL skills, including the ability to write complex queries for validation and analysis.
• Hands‑on experience testing data extractions, reports, ETL pipelines, or data migrations.
• Experience working as part of Agile Scrum teams.
• Strong understanding of SDLC, Agile workflows, and defect management processes.
• High attention to detail and strong analytical thinking skills.
• Ability to communicate data quality risks clearly to technical and non‑technical stakeholders.

Preferred Qualifications

• Experience in healthcare, regulated environments, or data‑intensive platforms.
• Familiarity with healthcare data concepts, compliance considerations, or reporting requirements.
• Experience validating large or complex datasets across shared databases.
• Exposure to automation‑friendly test design for data pipelines.
• Experience using AI‑assisted tools to support test case creation or data analysis.