Sr. Data Quality Analyst

McKesson•Atlanta, GA

2d•$104,600 - $174,400

About The Position

CoverMyMeds is seeking a Senior Data Quality Analyst to build and mature a comprehensive data quality capability from the ground up. Working closely with Data Domain Officers, business stakeholders, data teams, and contractors, you’ll translate business expectations into practical standards, processes, quality rules, dashboards, and services that can be implemented across our data platforms and business lines. This highly visible opportunity is ideal for a collaborative, creative self-starter who is comfortable navigating ambiguity and turning data quality needs into scalable solutions.

Requirements

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 (e.g., Collibra, Informatica, Monte Carlo, Great Expectations, Soda) and catalog/lineage tools (e.g., Alation, Informatica Axon)
Proficiency with SQL and at least one scripting language (Python preferred) for building and automating quality checks

Nice To Haves

Knowledge of healthcare industry data standards and regulations (e.g., HIPAA) is highly desirable
Data quality or governance certification (e.g., 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

Responsibilities

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 (e.g., 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 (e.g., 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