Senior Data Integrity Engineer

Role Description

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 recommendations more trustworthy — and dentists notice.

Qualifications

• Senior-level engineering skills — you’re a strong programmer, not an analyst who can script. You can build production systems, not just notebooks.
• Node.js proficiency — TypeScript experience is a plus. Our stack is Node, AWS Lambda, PostgreSQL, with Snowflake available for analytical workloads.
• Strong SQL skills — you’ll live in PG and Snowflake, exploring large datasets to find patterns in what’s missing or wrong.
• Product mindset — you think about data quality in terms of “what does this do to the user’s experience,” not just “is this field populated.” You’ll need to understand our product deeply to do this job well.
• Self-directed problem finder — you don’t wait to be told what’s broken. You dig, you notice, you quantify, and you propose the fix.
• Comfortable owning vendor relationships — you’ll be on calls with data providers, writing up findings, pushing for fixes. This requires clarity, persistence, and professionalism.
• Enthusiastic about agentic software development — you actively use tools like Cursor and Claude Code to multiply your output. This isn’t optional or aspirational; it’s how you work today.

Requirements

• Analyze incoming vendor data across every domain we integrate with to identify omissions, inaccuracies, and inconsistencies — the data is complex, and the problems are often subtle.
• Build automated data quality monitoring and alerting — design and implement systems that catch problems before they reach our customers.
• Measure and improve the precision and recall of our recommendations — quantify the impact of data issues, prioritize by product impact, and track improvement over time.
• Own vendor data quality relationships — work directly with data providers through structured reporting, regular syncs, and formal SLAs to drive upstream fixes.
• Proactively detect stale or broken data connections and find ways to reduce the manual overhead required to keep them healthy.
• Write application code to validate, transform, and reconcile incoming data — not just queries, but programmatic detection and correction systems.
• Communicate findings to engineering, product, and leadership — translate data problems into product impact that drives prioritization.

Benefits

• Competitive compensation and benefits.
• Ongoing training and development opportunities.
• Flexible, uncapped PTO.
• Remote work environment.