Pearl
Healthcare
SeniorDataIntegrityEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Integrity Engineer at Pearl. Skills: Data integrity, Data quality monitoring, Vendor data management. Analyze vendor data. Identify omissions”
What You'll Achieve.
Make the data right; Make recommendations more trustworthy
Industry & Context.
Root cause analysis; Troubleshooting
What They're Looking For.
Must Have
Senior-level engineering skills, Node.js proficiency, SQL skills, Product mindset, Self-directed problem finder, Comfortable owning vendor relationships, Enthusiastic about agentic software development
Nice to Have
TypeScript experience is a plus, Healthcare or insurance domain knowledge is a plus
What You'll Do.
Identify inaccuracies
Identify inconsistencies
Build automated data quality monitoring
Build alerting systems
Prioritize by product impact
Own vendor data quality relationships
Work with data providers
Detect stale data connections
Detect broken data connections
Reduce manual overhead
Write application code
Validate incoming data
Transform incoming data
Reconcile incoming data
Programmatic detection systems
Programmatic correction systems
Translate data problems
How You'll Work.
Team & Collaboration
Engineering; Product; Leadership
Communication Scope
Structured reporting; Formal SLAs
Full Job Description
ABOUT PEARL Pearl is shaping the future of dentistry with a suite of AI solutions developed to establish higher standards of quality and care for patients worldwide. Since 2019, our team has engineered FDA-cleared computer vision capabilities for the interpretation of 2D and 3D dental imagery — industry-leading capabilities which clinicians, practice owners, labs, and insurers use to elevate the efficiency, accuracy, and consistency of dental care around the world. At Pearl, we believe AI isn’t just what we build—it’s how we build. We’re looking for engineers who embrace AI-powered development tools to amplify their output and ship better software, faster. THE ROLE Dental insurance is messy. Every payer has different rules, different data formats, and different ideas about what they’ll cover and when. We’re building AI that cuts through that complexity so dentists can focus on patients instead of navigating insurance. Our product sits at the intersection of clinical decisions and insurance data — and the quality of our recommendations lives or dies on the quality of that data. The data is high-cardinality, spans multiple domains, and comes from vendors with varying levels of reliability. Fields are missing. Values contradict each other. Connections go stale. We want to catch these problems before our customers ever feel them — and unlock new value by trusting the data enough to build on it. We need someone who owns this problem end to end. Not as a side project for the engineering team, but as the job. Think of it as detective work backed by engineering. You’ll trace bad recommendations back to their source, build systems that catch problems before customers do, and hold vendors accountable for the data they send us. Some days that’s writing code, some days it’s writing a report for a vendor call — but the mission is always the same: make the data right. This role has a direct line from your work to the product getting smarter. Every data problem you solve makes our
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