Senior QA Engineer

When data moves through a pipeline, someone has to verify it actually arrived correctly, right values, right shape, right place, no silent failures. This role owns that answer across the data layers that power our clients' clinical and operational decisions.

H3Tech (Healthcare, High-tech, Human) partners with HealthTech companies, insurers, providers, and life sciences organizations to build software that serves clinicians, patients, and administrators. Fully remote, AI-era engineering, with people making every decision that matters.

The Role

You hold data quality across one client engagement. You design and run the validation strategy for ETL pipelines, data warehouses, and downstream reporting, catching integrity failures before they reach production dashboards or clinical workflows. You're not a downstream checker; you define the quality bar at pipeline design time and enforce it at every transformation step.

What You're Responsible For

• Design and maintain automated and manual validation suites for ETL pipelines feeding data warehouses and BI layers
• Define data quality SLAs: field-level validation rules, completeness checks, and reconciliation thresholds, agreed before pipelines go live
• Lead QA gating for data and reporting releases, make the call on whether data reaches production
• Validate data mappings and transformations between source systems and target warehouses at every layer
• Investigate data discrepancies: root-cause analysis, clear defect documentation, and remediation tracking
• Build regression coverage for recurring reporting workflows, executive dashboards, campaign reporting, operational feeds
• Contribute to the QA playbook and data governance documentation for the engagement

Qualifications

• Required
  • Bachelor's degree or above in IT, computer science, information systems, or a related field
  • 5+ years in data quality assurance, data testing, or data engineering with a QA focus, candidates without hands-on data pipeline experience will not meet the bar for this track
  • Comfortable reviewing AI-generated data pipeline code and transformation artifacts at volume, catching correctness and integrity issues at pace
  • Business-level English, defect documentation and client-facing release notes are in English
• Technical Skills
  • SQL: complex validation and reconciliation queries against large-scale datasets, not just SELECT statements
  • ETL testing: hands-on experience validating pipeline transformations in Snowflake, Redshift, BigQuery, or equivalent, this is a must, not a nice-to-have
  • Test automation for data: dbt tests, Great Expectations, pytest, or equivalent data quality frameworks
  • Defect management: Jira or equivalent; clear reproduction steps and impact assessment on every defect
  • Can distinguish a data quality defect from a business logic change from a source system issue, and route each correctly
• Domain & Compliance
  • Working knowledge of data governance, PII handling, and compliance requirements in regulated industries, HIPAA, SOC 2, or similar
• Strong Advantage
  • Experience in healthcare, pharmaceutical, or life sciences data environments
  • CI/CD integration for data testing: dbt Cloud, Airflow, or equivalent orchestration pipelines
  • Familiarity with BI tools (Tableau, Power BI) and validating dashboard-level metrics against source data