Senior Data QA Engineer

About Us Abacus Insights is transforming how data works for health plans. Our mission is simple: make healthcare data usable, so the people responsible for care and cost decisions can act faster, with confidence. We help health plans break down data silos to create a single, trusted data foundation. That foundation powers better decisions—so plans can improve outcomes, reduce waste, and deliver better experiences for members and providers alike. Backed by $100M from top investors, we’re tackling big challenges in an industry that’s ready for change. Our platform enables GenAI use cases by delivering clean, connected, and reliable healthcare data to support automation, prioritization, and decision workflows—and it’s why we are leading the way. Our innovation begins with people. We are bold, curious, and collaborative—because the best ideas come from working together. We embrace the thoughtful use of AI and automation to drive innovation and efficiency, and we look for individuals who are curious and adaptable—those excited to leverage emerging technologies to enhance how we work—while keeping human insight, connection, and our clients at the center of every decision. Ready to make an impact? Join us and let’s build the future together. About the Role As a Senior Data Quality Engineer at Abacus Insights, you will own the accuracy, reliability, and compliance of healthcare data powering our cloud-native data management platform. This role requires deep expertise in data engineering, data quality architecture, and healthcare data domains. You will architect automated testing frameworks, lead data validation strategy, and partner with Engineering and Product leadership to maintain high-trust, high-quality datasets for health plan clients at scale. You will also mentor junior QA engineers and help shape the broader data quality practice across Abacus's data ecosystem, directly supporting regulatory and operational integrity. Your day to day 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 What you bring to the team 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 What we would like to see, but not required Deep exposure to Delta Lake, Spark, Airflow, dbt, or event-driven architectures Advanced knowledge of schema evolution management (Parquet, Avro, ORC, JSON) Experience leading data quality lifecycle management initiatives in large-scale cloud systems Familiarity with Terraform, DevOps pipelines, CI/CD workflows, Git-based version control Background in software debugging, system testing methodologies, or performance testing at an architectural l