Senior Data QA Engineer

Abacus Insights•Boston, MA

2d•Remote

About The Position

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.

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

Nice To Haves

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

Responsibilities

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