QA Manager in Pittsburgh, PA (Hybrid) - Must be local - W2 / own corporation only

QA Manager

Location: Pittsburgh, PA (Hybrid)

Contract

Need to Report on office for first week (Onboarding)

Purpose

The Manager, Quality Assurance, Data leads the data-side QA function within the enterprise Quality Assurance organization, owning test strategy, test planning, and test execution for data pipelines, data products, and analytics outputs. This role is responsible for maturing the Data QA practice from basic scripting to structured, ASTQB-aligned methodology: formal test plans, documented test cases, regression coverage, and measurable data quality validation against governance-defined quality dimensions. The Manager works in close partnership with Data & Analytics and Data Governance to embed validation checkpoints across the full data lifecycle.

Responsibilities

Data QA Strategy & Methodology

• Owns the test strategy, test planning standards, and test execution methodology for the data QA track, aligned to ASTQB principles and the enterprise QA framework.
• Matures the Data QA function from ad-hoc scripting to structured test plans, formal test case documentation, and regression coverage across data pipelines, transformation layers, and analytics outputs.
• Defines data quality validation checkpoints at ingestion, transformation, and delivery layers in partnership with Data & Analytics engineering leadership.
• Operationalizes governance-defined data quality dimensions (completeness, accuracy, timeliness, consistency, uniqueness) as executable test criteria in pipeline validation suites.

Test Execution & Pipeline Validation

• Leads the development and execution of formal test suites for Tier 1 and Tier 2 data pipelines, progressing from basic null/type/row-count checks to structured validation coverage.
• Establishes regression coverage for data pipelines, ensuring that changes to pipeline logic, data models, or source systems do not introduce undetected quality regressions.
• Partners with Data Engineering and Analytics Engineering on pipeline architecture decisions that affect validation feasibility, observability, and quality checkpoints.
• Manages defect tracking and reporting for data quality issues, including severity classification, escalation thresholds, and root cause review cadence.

Tooling & Automation

• Drives the automation strategy for data pipeline validation, targeting regression and contract-based testing as primary automation surfaces.
• Evaluates, selects, and implements data QA tooling in collaboration with the Director, QA and Data & Analytics leadership (e.g., Great Expectations, dbt tests, Soda, Monte Carlo integration).
• Establishes data quality metrics reporting: pipeline validation coverage, defect rates, data quality incident trends, and escape rates reported to IT leadership.

Cross-Functional Partnership

• Partners with Data Governance to align validation rules with governance-defined quality standards, ensuring QA operationalizes policy rather than defining it independently.
• Partners with Informatics to ensure data pipeline acceptance criteria are testable and actionable at intake, before work enters the data engineering delivery pipeline.
• Participates in joint retrospectives with Data & Analytics and Governance leadership to identify upstream pipeline design or requirements failures contributing to data quality incidents.

Required Qualifications

• 7+ years of progressive QA experience, with at least 3 years focused on data quality, data pipeline validation, or analytics QA in a structured delivery environment.
• Demonstrated experience maturing data QA practices: moving from ad-hoc scripting to formal test plans, documented test cases, and regression coverage.
• Hands-on experience with data quality testing frameworks and tooling (Great Expectations, dbt tests, Soda, or equivalent).
• Deep familiarity with data quality dimensions (completeness, accuracy, timeliness, consistency, uniqueness) and how they translate into executable validation rules.
• Experience with cloud-based data platforms (Azure, AWS, or Google Cloud Platform) and modern data stack environments (Databricks, Snowflake, or equivalent).
• Proven ability to lead QA teams and build adoption of structured QA methodology across peer engineering functions.
• At least 2 years in a people management or team lead capacity, with experience hiring, developing, and retaining QA practitioners.

Preferred Qualifications

• ISTQB/ASTQB certification (Foundation, Advanced, or Expert level).
• Experience in healthcare, specialty pharmacy, or regulated industries with exposure to clinical or operational data environments.
• Familiarity with data governance frameworks (DAMA-DMBOK) and experience operationalizing governance-defined quality standards in pipeline validation.
• Exposure to pipeline observability platforms (Monte Carlo, Datadog, or equivalent) and ODCS or data contract testing patterns.
• Familiarity with Databricks medallion architecture, Unity Catalog, and Delta Lake in production environments.
• Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related field, or equivalent experience.