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# Data Quality Analyst

- **Company:** CareerCircle
- **Location:** Irving, TX, 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.careercircle.com/jobs/all/all/usa/all/all/5c96d1e4-10c1-4ea6-a2cd-e1a34683972b

## About the Role

Tooling
Advising
Dashboard
Scripting
Automation
Governance
Scalability
Testability
Informatica
Data Domain
Data Quality
Self-Starter
Communication
Observability
Service Layer
Data Pipelines
Problem Solving
Data Governance
Computer Science
Data Engineering
Data Architecture
Anomaly Detection
Collibra (Software)
Alation Data Catalog
Regulatory Compliance
Information Technology
Business Administration
Data Quality Assessment
SQL (Programming Language)
Extract Transform Load (ETL)
Python (Programming Language)
Healthcare Industry Knowledge
Key Performance Indicators (KPIs)
General Data Protection Regulation (GDPR)
Health Insurance Portability And Accountability Act (HIPAA) Compliance, * 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

Preferred Experience / Skils:

 * 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

## 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 (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, Tooling
Advising
Dashboard
Scripting
Automation
Governance
Scalability
Testability
Informatica
Data Domain
Data Quality
Self-Starter
Communication
Observability
Service Layer
Data Pipelines
Problem Solving
Data Governance
Computer Science
Data Engineering
Data Architecture
Anomaly Detection
Collibra (Software)
Alation Data Catalog
Regulatory Compliance
Information Technology
Business Administration
Data Quality Assessment
SQL (Programming Language)
Extract Transform Load (ETL)
Python (Programming Language)
Healthcare Industry Knowledge
Key Performance Indicators (KPIs)
General Data Protection Regulation (GDPR)
Health Insurance Portability And Accountability Act (HIPAA) Compliance
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