[Hiring] Data Quality Engineer / Data Scientist @Vervic Inc.

Role 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.
• Enable rapid identification of integration defects and reliable validation across VA systems and external partner boundaries.

Qualifications

• 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.

Requirements

• 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.

Benefits

• 401(k)
• 401(k) matching
• Dental insurance
• Health insurance
• Paid time off

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)

• 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.