About the job
About Us
At Abacus Insights, we are revolutionizing the utilization of data in healthcare for health plans. Our mission is straightforward: to make healthcare data actionable, enabling decision-makers to respond swiftly and confidently. We empower health plans to dismantle data silos, creating a reliable and unified data foundation that enhances decision-making—leading to improved outcomes, reduced inefficiencies, and superior experiences for members and providers alike.
With strong backing of $100 million from leading investors, we are taking on significant challenges in an industry poised for transformation. Our innovative platform fosters GenAI applications by providing clean, interconnected, and dependable healthcare data that supports automation, prioritization, and decision workflows—positioning us as industry leaders.
Innovation is driven by people. We embrace boldness, curiosity, and collaboration, as we believe that the best ideas emerge from teamwork. Are you ready to make a meaningful impact? Join us as we build the future together.
About the Role
As a Data Quality Engineer at Abacus Insights, you will play a crucial role in ensuring the accuracy, reliability, and compliance of healthcare data that powers our cloud-native data management platform. This position requires expertise in data engineering, data quality frameworks, and healthcare data domains. You will be responsible for designing automated testing procedures, creating data validation solutions, and collaborating with engineering and product teams to sustain high-trust, high-quality datasets for our health plan clients. Your contributions will directly reinforce the regulatory and operational integrity across Abacus’s data ecosystem.
Your day to day responsibilities include:
- Designing, developing, and maintaining automated data quality validation frameworks, including rules engines, anomaly detection, and monitoring for completeness, conformity, integrity, and timeliness.
- Building and maintaining automated test procedures for healthcare data ingestion, transformation, and downstream applications.
- Investigating data quality defects, analyzing root causes, and driving remediation recommendations.
- Reviewing and refining data quality strategies while contributing to standardized quality processes across pipelines.
- Collaborating with Engineering, Project Management, Operations, and Connector Engineering teams to ensure alignment and effectiveness in data quality initiatives.