Senior Data QA Engineer

Abacus Insights

Senior Data Quality Engineer at Abacus Insights ensuring compliance and accuracy of healthcare data. Architecting automated testing frameworks and leading data validation strategies for health plan clients.

Posted 7/22/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Data Quality Engineering and Data Engineering, with a strong focus on healthcare data types and compliance standards. Proficient in building and maintaining automated data quality validation frameworks and leading cross-functional collaboration to ensure data integrity and security.

Highest-signal resume keywords

Expert-Level SQL SkillsAutomation Scripting in Python or JavaCloud Computing Environments (AWS, Databricks)Data Quality Project LeadershipHealthcare Data Types Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Quality EngineeringData EngineeringAutomated Test StrategiesData Integration WorkflowsETL/ELT PipelinesData Mapping StrategyData ProfilingAnomaly DetectionRoot Cause AnalysisAdvanced QA Automation Frameworks

Soft Skills

Excellent Communication SkillsOrganizational SkillsPrioritization SkillsMentoringCross-Functional Collaboration

Tools & Technologies

SQLPythonJavaAWS (S3, EC2, SSM, Athena)DatabricksCloud-Native ToolingQA Dashboards

Industry Keywords

Healthcare TechnologyData Quality StandardsPHI HandlingHIPAA ComplianceSOC 2 ComplianceData SecurityPayer/Provider Environments

Tech Stack

Tools & technologies

AWSCloudEC2ETLJavaPythonSQL

About the role

Key responsibilities & impact

Architect, build, and maintain enterprise-scale automated data quality validation frameworks, including rules engines, anomaly detection, and monitoring for completeness, conformity, integrity, and timeliness
Lead design and implementation of automated test strategies for complex healthcare data ingestion, transformation, and downstream application pipelines
Drive root cause analysis on high-impact data quality defects, own remediation strategy, and prevent recurrence through systemic process improvements
Define and evolve data quality strategy, standards, and best practices across pipelines, influencing tooling and process decisions org-wide
Partner directly with Engineering, Product, Project Management, Operations, and Connector Engineering leadership to translate business and compliance requirements into technical test plans, functional specifications, and validation logic
Lead review of software and data defect reports, identify systemic problem areas, and establish standards for reproducible issue documentation
Design and maintain advanced QA automation frameworks and dashboards using SQL, Python, Java, and cloud-native tooling
Lead system verification protocol design and represent QA in cross-functional architecture and design discussions
Conduct advanced data mining and profiling on client-specific and healthcare datasets to proactively surface quality risks at scale
Own documentation strategy including test plans, validation criteria, rule catalogs, and QA runbooks
Mentor and provide technical guidance to junior and mid-level QA engineers
Serve as an escalation point for internal and external data quality inquiries
Ensure data security and quality processes align with PHI handling, HIPAA, SOC 2, and Abacus governance requirements, and help evolve governance standards as the platform scales

Requirements

What you’ll need

Bachelor's or Master's degree in Computer Science, Information Systems, Data Analytics, or related technical field, or equivalent work experience.
6–8+ years of experience in Data Quality Engineering and Data Engineering, with significant experience in healthcare technology or payer/provider environments
Expert-level SQL skills, including complex data manipulation, validation, and profiling at scale
Proven ability to lead data quality projects end-to-end.
Deep experience working with healthcare data types such as enrollment, medical claims, pharmacy claims, provider data, or non-traditional health and wellness datasets
Strong hands-on automation scripting expertise in Python or Java, with a track record of building reusable frameworks
Proven experience with cloud computing environments such as AWS (S3, EC2, SSM, Athena) and Databricks in production-scale settings
Demonstrated experience designing data integration workflows, ETL/ELT pipelines, data mapping strategy, and enterprise QA testing protocols
Track record of building and scaling automated QA applications, dashboards, or custom rule frameworks from the ground up
Proven ability to analyze complex, large-scale datasets, identify systemic quality issues, and drive actionable, measurable improvements
Experience mentoring engineers and influencing technical direction across teams
Excellent communication skills, with the ability to work cross-functionally, influence stakeholders, and operate independently with minimal oversight
Strong organizational and prioritization skills in a fast-paced, multi-project environment

Benefits

Comp & perks

Unlimited paid time off – recharge when you need it
Work from anywhere – flexibility to fit your life
Comprehensive health coverage – multiple plan options to choose from
Equity for every employee – share in our success
Growth-focused environment – your development matters here
Home office setup allowance – one-time support to get you started
Monthly cell phone allowance – stay connected with ease