Senior Data QA Engineer

About the role

As a Senior Data QA Engineer you will own the accuracy, reliability, and compliance of the healthcare data powering Abacus’s platform, designing and scaling automated data quality validation frameworks, leading testing strategies, partnering with engineering and product leaders, mentoring junior QA engineers, and shaping the organization’s data quality practice to support regulatory and operational integrity.

About the company

Abacus Insights is a technology company that transforms healthcare data for health plans, building a cloud‑native platform that breaks down data silos, delivers clean, connected, and reliable data for decision‑making and GenAI use cases. Backed by $100 M of investor funding, the company emphasizes a bold, curious, collaborative culture that leverages AI and automation while keeping human insight and client focus central.

Requirements

• 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
• 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 level