Data QA Engineer at Abacusinsights

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 that can 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. Ready to make an impact? Join us and let's build the future together. About the Role As a Data Quality Engineer at Abacus Insights, you will ensure the accuracy, reliability, and compliance of healthcare data powering our cloud‑native data management platform. This role requires specialized knowledge in data engineering, data quality frameworks, and healthcare data domains. You will design automated testing procedures, build data validation solutions, and collaborate with engineering and product teams to maintain high‑trust, high‑quality datasets for health plan clients. Your work directly supports regulatory and operational integrity across Abacus’s data ecosystem. Your day to day • Design, develop, and maintain automated data quality validation frameworks, including rules engines, anomaly detection, and monitoring for completeness, conformity, integrity, and timeliness • Build and maintain automated test procedures for healthcare data ingestion, transformation, and downstream applications • Investigate data quality defects, analyze root causes, and drive remediation recommendations • Review and refine data quality strategies and contribute to standardized quality processes across pipelines • Collaborate with Engineering, Project Management, Operations, and Connector Engineering teams to translate business and compliance rules into technical test plans, functional specifications, and validation logic • Review software and data defect reports, highlight problem areas, and document reproducible issues • Develop and maintain QA automation scripts and dashboards using SQL, Python, Java, and cloud‑native tools • Participate in system verification protocol design and cross‑functional design meetings • Conduct data mining and profiling on client‑specific and healthcare datasets to identify quality risks and gaps • Produce documentation including test plans, validation criteria, rule catalogs, and QA runbooks • Support ongoing monitoring of data quality and respond to internal and external data inquiries • Ensure data security and quality processes align with PHI handling, HIPAA, SOC 2, and Abacus governance requirements What You Bring to the Team • Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Analytics, or related technical field • 3–5+ years of experience in Data Quality, Data Engineering, or QA roles in healthcare technology or payer/provider environments • Strong SQL expertise, including data manipulation, data validation, and profiling at scale • Experience working with healthcare data types such as enrollment, medical claims, pharmacy claims, provider data, or non‑traditional health and wellness datasets • Hands‑on experience with automation scripting in Python or Java • Experience with cloud computing environments such as AWS (S3, EC2, SSM, Athena) and Databricks • Experience with data integration workflows, ETL/ELT pipelines, data mapping, and QA testing protocols • Experience building automated QA applications, dashboards, or custom rule frameworks • Ability to analyze complex datasets, identify quality issues, and generate actionable insights • Excellent communication skills with the ability to work cross‑functionally and independently • Strong organizational skills to manage multiple priorities and deadlines What we would like to see, but not required • Exposure to Delta Lake, Spark, Airflow, dbt, or event‑driven architectures • Knowledge of schema evolution management (Parquet, Avro, ORC, JSON) • Experience with advanced data quality lifecycle management 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 What you’ll get in return • Competitive Leave & Benefits • Comprehensive health coverage • Equity for every employee – share in our success • Growth-focused environment – your development matters here Work arrangements • Standard hours: 8 hours/day, 5 days/week • Location: Pune, Hybrid (3 days a week in office) • Shift: 12pm-9pm IST Our Commitment as an Equal Opportunity Employer As a mission-led technology company helping to drive better healthcare outcomes, Abacus Insights believes that the best innovation and value we can bring to our customers comes from diverse ideas, thoughts, experiences, and perspectives. Therefore, we dedicate resources to building diverse teams and providing equal employment opportunities to all applicants. Abacus prohibits discrimination and harassment regarding race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. At the heart of who we are is a commitment to continuously and intentionally building an inclusive culture—one that empowers every team member across the globe to do their best work and bring their authentic selves. We carry that same commitment into our hiring process, aiming to create an interview experience where you feel comfortable and confident showcasing your strengths. If there’s anything we can do to support that—big or small—please let us know.