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# Senior Data Quality Analyst

- **Company:** McKesson
- **Location:** Atlanta, GA, United States
- **Experience:** Expert
- **Salary:** $104,600.0 - $174,400.0
- **Contract:** Permanent contract
- **Skills:** Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Python (Programming Language), DataOps, Service Layer, SQL Databases, Data Streaming, Datadog, Scripting, Great Expectations (Foster Youth College-readiness and Support Program), Mttr, Build Management, Information Technology, Collibra, Data Management, Data Pipelines
- **Published:** October 7, 2026
- **Apply:** https://www.careerboard.com/us/en/find-jobs-in-United-States/-BA3BBABD9E43DD065E/

## About the Role

* Bachelor's degree in Information Technology, Computer Science, Business Administration, or a related field (Master's degree preferred) or related experience
 * Typically requires 7+ years of experience in data quality, data observability, data engineering, or data governance, including direct experience building (not just documenting) a data quality capability within a large, complex organization
 * Proven experience designing data quality frameworks and implementing data observability tooling and practices
 * Strong understanding of data pipelines, data architecture, and how quality and observability checks integrate into ETL/ELT and streaming workflows
 * Hands-on experience with data quality/observability platforms (eg, Collibra, Informatica, Monte Carlo, Great Expectations, Soda) and catalog/lineage tools (eg, Alation, Informatica Axon)
 * Proficiency with SQL and at least one Scripting language (Python preferred) for building and automating quality checks

Preferred Experience/Skils:

 * Knowledge of healthcare industry data standards and regulations (eg, HIPAA) is highly desirable
 * Data quality or governance certification (eg, CDMP) is a plus
 * Excellent analytical and root-cause problem-solving skills - able to trace a quality defect from symptom to source across systems
 * Strong communication skills, able to translate technical quality/observability findings into business-relevant risk and action for both technical and non-technical stakeholders
 * Demonstrated ability to drive adoption of a shared capability across teams without direct authority over them

## Description

* Design and build the enterprise data quality framework - dimensions, scoring methodology, thresholds, and escalation paths - so that quality is measured consistently across domains rather than ad hoc per team
 * Stand up data observability capability (freshness, volume, schema drift, distribution, lineage-aware anomaly detection) across priority pipelines and datasets, so quality issues surface before they reach consumers
 * Define and operationalize data quality rules and checks for critical data elements, working with data owners and stewards to encode business meaning into testable logic
 * Build the data quality service layer: reusable rule libraries, monitoring dashboards, and alerting that downstream teams can plug into rather than rebuild
 * Conduct data quality assessments, triage root causes (source, pipeline, model, or definitional), and drive remediation plans with data owners and engineering teams through to closure
 * Evaluate, implement, and administer data quality and observability tooling (eg, Collibra, Informatica, Monte Carlo, Great Expectations) as part of the broader data catalog and lineage stack
 * Establish data quality SLAs/SLOs with data owners and platform teams, and report quality and observability KPIs (completeness, accuracy, timeliness, incident MTTR) to track the health of the capability over time
 * Partner with legal, compliance, and privacy teams to ensure quality controls meet regulatory requirements (eg, HIPAA, GDPR, CCPA) without duplicating existing governance processes.
 * Train data stewards and business users on the data quality framework, tooling, and how to interpret observability signals and escalate issues
 * Act as the subject matter expert on data quality and observability practices, advising stakeholders on how to design new systems and pipelines with quality built in rather than bolted on
 * Proactively identify systemic data quality risks (not just individual incidents) and escalate capability gaps to leadership