Data Quality Assurance (QA) & Testing Manager

Work Schedule

Environmental Conditions

Job Description

Data Quality Assurance (QA) & Testing Manager

Enterprise Data Platforms, Integration & AI-Enabled Test Automation

At Thermo Fisher’s PPD clinical research business, we’re using digital innovation, data science, and AI to reimagine how life-changing therapies reach patients. Our teams combine deep scientific expertise with advanced analytics, automation, and digital platforms to make research smarter, faster, and more connected.

We know that innovation happens when diverse minds meet. Our Digital Science, Data, and AI professionals collaborate closely with scientists, clinicians, and operational experts to solve real-world challenges in clinical research. Alongside our partnership with Open AI, you can be part of the collaboration that will help to improve the speed and success of drug development, enabling customers to get medicines to patients faster and more cost effectively.

This is a remote position for qualified candidates in the US (preference for CST or EST).

About the role:

We are seeking a hands-on Data Quality Assurance (QA) & Testing Manager (known internally as IT Manager, band 7) to lead testing strategy, execution, automation, and quality controls for enterprise data ingestion, integration, transformation, storage, and consumption. This leader will ensure that data is accurate, complete, reliable, secure, traceable, and fit for analytics, modeling, AI, and operational use.

The environment includes Talend, MuleSoft, service-oriented architecture (SOA), REST/SOAP APIs, file ingestion, point-to-point interfaces, and ETL/ELT pipelines, with Snowflake and Databricks as preferred platform experience. The role requires advanced SQL, Python, practical use of AI-assisted testing tools, and disciplined validation in a regulated environment.

We seek a technically credible, pragmatic leader who can challenge designs constructively, prioritize risk, build reusable QA capabilities, mentor teams, and communicate data quality issues in business terms. The successful candidate will establish a proactive quality culture—not simply manage test execution.

Data QA Strategy & Leadership

• Own data platform and integrations QA strategy, testing standards, planning, quality gates, defect management, and release-readiness recommendations.
• Lead and coach QA analysts and testing engineers; manage priorities, capacity, stakeholder expectations, and delivery risk.
• Partner with data engineering, integration, domain leads, architecture, security, and governance teams to shift quality controls earlier in delivery.

SQL Validation & Reconciliation

• Design and execute advanced SQL validation of source-to-target mappings, joins, transformations, aggregates, business rules, and referential integrity.
• Apply repeatable data validation and reconciliation practices, maintaining test cases, results, exceptions, evidence, and approvals.
• Validate record counts, completeness, duplicates, incremental loads, schema changes, and downstream consistency; investigate discrepancies to root cause.

ETL, API & Integration Testing

• Lead end-to-end testing for all solutions and changes delivered by the Integrations, RTE Development, and Platform teams, including Talend ETL/ELT workflows, MuleSoft and SOA services, REST/SOAP APIs, file-based ingestion, and point-to-point connections.
• Validate authentication, payloads, contracts, file layouts, scheduling, retries, error handling, recovery, and upstream/downstream dependencies.
• Coordinate integration, regression, performance-relevant, and release testing across batch and near-real-time data flows.

SQL/Python, Automation & AI-Assisted Testing

• Build and maintain Python/SQL automation for reconciliation, transformation checks, API tests, schema validation, and repeatable regression suites.
• Integrate automated checks into CI/CD pipelines and establish actionable reporting of failures, coverage, and quality trends.
• Use enterprise-approved AI-assisted tools to accelerate test design, SQL/Python script development, defect triage, and anomaly investigation.
• Evaluate AI-generated test artifacts for accuracy, coverage, security, and reproducibility; require human review and documented validation for controlled releases.

Data Platforms, Governance & Compliance

• Validate cloud data ingestion and transformations across enterprise platforms; Snowflake and Databricks knowledge is preferred.
• Ensure testing supports lineage, traceability, access controls, sensitive-data protection, change management, and audit readiness.
• Maintain evidence of testing, defects, remediation, approvals, and release decisions in accordance with applicable enterprise and regulatory controls.

Required Qualifications:

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent and relevant formal academic / vocational qualification.
• Previous experience in data QA, ETL/integration testing, data engineering testing, or related roles that provides the knowledge, skills, and abilities to perform the job (comparable to 6+ years’ experience)
• 3+ years leading QA teams or major testing programs.
• Expert SQL skills: complex joins, CTEs, window functions, aggregation, source-to-target reconciliation, and query-based data validation.
• Strong Python skills and experience creating or maintaining automated data/API testing frameworks.
• Familiarity with the Software Development Life Cycle (SDLC), ETL/ELT testing, Talend, MuleSoft, SOA, APIs, file ingestion, and point-to-point interfaces.
• Practical use of AI-assisted coding or testing tools and ability to independently verify their outputs.
• Experience with test planning, regression automation, CI/CD, defect triage, stakeholder communication, and release quality decisions.
• Experience maintaining documented controls, testing evidence, and change discipline in a regulated environment.

Preferred Qualifications:

• Snowflake and Databricks experience, including testing ingestion, transformations, and downstream data consumption.

• Exposure to Apache Spark/PySpark, Apache Iceberg, lakehouse architecture, and multi-platform data access.

• Experience with modern AI-enabled testing tools and test automation frameworks.

• Familiarity with AI-assisted development tools, automated anomaly detection, data observability, and enterprise AI governance.

• Experience testing data products supporting analytics, machine learning, and AI use cases.

Success Measures:

• Increased automated regression and data validation coverage with reduced manual test effort.
• Fewer escaped production defects and faster identification and resolution of data discrepancies.
• Reliable, auditable source-to-target validation and release evidence.
• Improved test cycle time, quality metrics, and stakeholder confidence in enterprise data.
• Responsible, measurable adoption of AI-enabled QA practices without weakening regulated controls.

At Thermo Fisher Scientific, we are committed to fostering a healthy and harmonious workplace for our employees. We understand the importance of creating an environment that allows individuals to excel. Please see below for the required qualifications for this position, which also includes the possibility of equivalent experience:

• Able to communicate, receive, and understand information and ideas with diverse groups of people in a comprehensible and reasonable manner.
• Able to work upright and stationary for typical working hours.
• Ability to use and learn standard office equipment and technology with proficiency.
• Able to perform successfully under pressure while prioritizing and handling multiple projects or activities.
• May require as-needed travel (0-20%).

Location: Remote US. Relocation assistance is NOT provided.

*Must be legally authorized to work in the United States without sponsorship.

*Must be able to pass a comprehensive background check, which includes a drug screening.

The annual salary range estimated for this position in North Carolina is $105,000 - $155,000 USD. This position may also be eligible to receive a variable annual bonus based on company, team, and/or individual performance results in accordance with company policy.