Senior Data Quality Assurance Engineer

Role Overview

Join Abacus Insights as a Senior Data Quality Assurance Engineer to ensure the accuracy, reliability, and compliance of healthcare data. You will architect automated validation frameworks, lead data quality strategy, and partner with Engineering and Product leadership to maintain high-quality datasets for health plan clients.

Key Missions & Responsibilities

• Architect, build, and maintain enterprise-scale automated data quality validation frameworks, including:
  • rules engines
  • anomaly detection
  • monitoring for completeness, conformity, integrity, and timeliness
• Design and implement automated test strategies for healthcare data ingestion, transformation, and downstream pipelines.
• Drive root-cause analysis for high-impact data quality defects, own remediation strategy, and prevent recurrence via systemic process improvements.
• Lead data quality projects end-to-end and shape the broader data quality practice across the data ecosystem.
• Mentor junior QA engineers and influence technical direction across teams.

Requirements

• Experience designing data integration workflows, ETL/ELT pipelines, data mapping strategies, and enterprise QA testing protocols.
• Deep experience with healthcare data types such as enrollment, medical claims, pharmacy claims, provider data, or similar health/wellness datasets.
• Expert SQL skills for validation, profiling, and manipulation at scale.
• Ability to mentor engineers and influence technical direction.
• Strong cross-functional communication and ability to operate independently with minimal oversight.
• Hands-on automation scripting experience in Python or Java, with a track record of building reusable frameworks.
• Production experience with cloud environments, especially AWS and Databricks.
• Bachelor’s or Master’s degree in a related technical field (or equivalent experience).
• Proven ability to analyze large-scale datasets, identify systemic quality issues, and drive measurable improvements.
• Track record building and scaling automated QA applications/dashboards/rule frameworks.
• 6–8+ years of experience in Data Quality Engineering and Data Engineering, with significant healthcare technology/payer/provider exposure.

Nice-to-Haves

• Strong exposure to Delta Lake, Spark, Airflow, dbt, or event-driven architectures.
• Advanced knowledge of schema evolution (e.g., Parquet, Avro, ORC, JSON).
• Familiarity with Terraform, DevOps pipelines, and CI/CD workflows with Git-based version control.
• Background in system testing methodologies, debugging, or performance testing at an architectural level.

Location / Work Model

• Full remote (listed with New York, US).