_{22nd August, 2026}

Role: Data Quality Engineer / Data Scientist.
Location: 100% Remote.
 
Job Description:
The Data Quality Engineer / Data Scientist is responsible for establishing and maintaining data quality, reconciliation, profiling, and analytical capabilities supporting the Community Care Network Next Generation (CCN Next Gen) Systems Integration Team.
 
This role works across the end-to-end CCN Next Gen ecosystem to ensure that data remains accurate, complete, consistent, traceable, and fit for purpose as it moves between applications, interfaces, transaction formats, and organizational boundaries.

The Data Quality Engineer / Data Scientist applies data engineering, statistical analysis, automation, and AI-assisted techniques to identify data anomalies, validate transformations, reconcile expected and actual results, and expose integration issues that may not be detectable through traditional functional testing alone.
 
Working as part of a multidisciplinary Systems Integration Team, this individual partners closely with Synthetic Data Engineers, Healthcare Interoperability Engineers, Automation Engineers, Integration Validation Engineers, business analysts, and existing CCN product teams to establish trusted data baselines and provide objective evidence of data quality throughout the complete transaction lifecycle.
 
The role initially supports end-to-end integration within the VA ecosystem and will expand to support data validation and reconciliation across external TPA and partner integrations.
 
Description Mission:
Establish a comprehensive, automated data quality and reconciliation capability that provides confidence in the accuracy, completeness, consistency, and integrity of data throughout CCN Next Gen end-to-end business transactions, enabling rapid identification of integration defects and reliable validation across VA systems and external partner boundaries.
 
Job Responsibilities:
 
Data Quality Engineering

• Design and implement reusable data quality frameworks supporting end-to-end systems integration.
• Define and automate data quality controls for accuracy, completeness, consistency, validity, uniqueness, timeliness, and referential integrity.
• Develop rules and metrics for evaluating data quality at critical points throughout business transaction lifecycles.
• Establish repeatable processes for identifying, classifying, prioritizing, and reporting data quality issues.
• Create automated quality gates that identify data defects before they propagate across downstream systems or integration boundaries.
• Maintain data quality evidence and metrics supporting integration readiness and release decisions.

End-to-End Data Reconciliation

• Develop reconciliation methods that compare source data, intermediate transformations, transactions, and downstream outcomes across multiple systems.
• Trace data elements through complex integration workflows to determine whether information is transformed and transmitted correctly.
• Establish expected-versus-actual reconciliation capabilities for prioritized CCN Next Gen business scenarios.
• Identify data loss, transformation errors, duplication, unexpected modification, and inconsistent business outcomes across system boundaries.
• Support reconciliation across VA and external TPA/partner boundaries as the E2E integration capability expands.
• Assist engineering teams in isolating the point at which data discrepancies are introduced.

Data Profiling & Analysis

• Profile structured and semi-structured healthcare data to establish baselines, distributions, relationships, patterns, and quality characteristics.
• Analyze large datasets to identify anomalies, outliers, unexpected patterns, and potential integration defects.
• Develop repeatable profiling routines that can be executed as systems, interfaces, and requirements evolve.
• Compare data characteristics across environments and processing stages to identify unexpected changes.
• Develop analytical methods that improve understanding of how data behaves throughout the CCN Next Gen ecosystem.

Synthetic Data Quality

• Partner with Senior Synthetic Data Engineers to establish measurable quality criteria for synthetic healthcare datasets.
• Evaluate synthetic datasets for internal consistency, referential integrity, scenario completeness, standards compliance, and fitness for intended testing purposes.
• Validate that synthetic scenarios maintain required relationships among members, providers, eligibility, authorizations, claims, payments, and other relevant entities.
• Compare synthetic data characteristics against approved reference characteristics where appropriate without introducing PHI into the synthetic data environment.
• Support certification and versioning of trusted synthetic datasets used within the CCN Next Gen Golden Scenario Library.

Statistical & Intelligent Quality Analysis

• Apply statistical techniques to identify anomalies, trends, correlations, and unexpected behavior within integration data.
• Develop automated methods for detecting data-quality conditions that may not be captured through deterministic business rules alone.
• Evaluate opportunities to apply machine learning, AI-assisted analysis, and intelligent automation to improve anomaly detection, reconciliation, root-cause analysis, and data-quality assessment.
• Develop explainable and repeatable analytical approaches suitable for use within a regulated federal healthcare environment.
• Ensure AI-assisted or statistical findings remain subject to appropriate validation and human review before influencing trusted engineering baselines.

Automation & Engineering

• Develop reusable Python, SQL, or comparable code supporting data profiling, validation, reconciliation, and analysis.
• Integrate data-quality checks into CI/CD pipelines and automated E2E validation workflows where appropriate.
• Develop reusable data-quality services, scripts, libraries, and analytical components.
• Partner with the Senior Automation Engineer to operationalize quality checks within automated integration workflows.
• Support dashboards, metrics, and reporting that provide visibility into data quality and reconciliation outcomes.

Defect Analysis & Root Cause Support

• Investigate data discrepancies discovered during integration and E2E validation.
• Distinguish among source-data defects, transformation issues, mapping errors, interface defects, business-rule inconsistencies, and downstream processing problems.
• Provide data-driven evidence that accelerates defect triage and root-cause analysis.
• Collaborate with CCN product teams to resolve data-quality issues and verify corrective actions.
• Identify recurring data-quality patterns and recommend systemic improvements.
• Cross-Team Collaboration & Surge Support
• Provide specialized data-quality and analytical support to CCN Next Gen product teams as integration needs emerge.
• Participate in E2E scenario planning, integration readiness reviews, defect triage, and release-readiness activities.
• Work with the Enterprise Healthcare Interoperability Engineer to ensure quality rules appropriately reflect healthcare standards and transaction semantics.
• Partner with Business Analysts to translate business requirements into measurable data-quality expectations.
• Support Integration Validation Engineers with data evidence required to determine whether complete business transactions produced the expected outcomes.
• Contribute to reusable engineering assets that improve data quality across the broader CCN Next Gen product line.

Required Skills:

• Strong proficiency with SQL and experience analyzing complex relational datasets.
• Programming experience using Python, R, or comparable analytical programming languages.
• Experience developing automated data validation, profiling, reconciliation, or quality-control processes.
• Strong understanding of data modeling, transformation, data lineage, and referential integrity concepts.
• Experience investigating complex data issues and performing root-cause analysis.
• Ability to translate business and technical requirements into measurable data-quality rules and validation criteria.
• Excellent analytical, problem-solving, communication, and collaboration skills.

Desired Qualifications:

• Experience supporting healthcare payer, provider, federal healthcare, or other complex healthcare data environments.
• Experience working with healthcare claims, eligibility, authorization, provider, payment, or clinical data.
• Familiarity with ANSI X12, NCPDP, HL7 FHIR, or other healthcare interoperability standards.
• Experience supporting end-to-end systems integration or integration testing.
• Experience validating synthetic data or developing synthetic-data quality methodologies.
• Experience with cloud-based data platforms and modern data engineering technologies.
• Experience integrating data-quality controls into CI/CD or DevSecOps pipelines.
• Experience with statistical anomaly detection, machine learning, or AI-assisted data analysis.
• Experience developing data-quality dashboards, observability capabilities, or automated reporting.
• Experience operating within federal, regulated, or security-sensitive environments.

Experience using AI-assisted development and analytical tools to improve engineering productivity.
 
Desired Technical Skills

• Python
• SQL
• R or comparable analytical languages
• Data profiling and data-quality frameworks
• Data reconciliation
• Statistical analysis
• Data modeling and transformation
• Data lineage and traceability
• Automated validation
• Anomaly detection
• REST APIs and integration concepts
• Cloud data platforms
• Git-based source control
• CI/CD and DevSecOps
• Data visualization and reporting
• Healthcare data and interoperability concepts

Education and Experience:

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Data Engineering, Information Systems, or a related technical discipline, or equivalent professional experience.
• Ten (10) or more years of experience in data engineering, data quality, data science, analytics engineering, or a related technical field.

Success Measures (First 12-18 Months)
The Data Quality Engineer / Data Scientist will be expected to:

• Establish reusable data-quality standards, metrics, and automated controls supporting CCN Next Gen E2E integration.
• Implement end-to-end data reconciliation capabilities across prioritized VA business transactions and system interfaces.
• Establish measurable quality baselines for the CCN Next Gen master synthetic dataset and Golden Scenario Library.
• Integrate automated data-quality checks into synthetic data generation and E2E validation workflows.
• Reduce the time required to identify and isolate data-related integration defects through automated profiling, reconciliation, and analytical techniques.
• Provide clear data lineage and evidence showing how critical information changes as it moves through prioritized E2E transaction lifecycles.
• Develop statistical and intelligent analytical capabilities that complement deterministic validation and identify previously undetected data-quality issues.
• Support expansion of data-quality and reconciliation capabilities from internal VA systems to TPA and external partner integrations.
• Establish reusable data-quality engineering assets that can be leveraged by existing CCN product teams.
• Improve overall confidence that data entering, traversing, and exiting the CCN Next Gen ecosystem remains accurate, complete, consistent, traceable, and fit for its intended business purpose.

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