Data Quality Automation Engineer

Pace Mate™ Data Quality Automation Engineer (full-time, remote)

Primary

Location:

All U.S. Locations (remote)

• Department: AI & Automation
• FLSA Type:
  Exempt
• Reports To:
  
  Head of AI, Innovation and Data
• Location:
  
  Remote (Must reside within the contiguous United States)

About Pace Mate

Pace Mate is a remote cardiac device monitoring company that plays a critical role in healthcare by enabling continuous monitoring of patients with cardiac devices. We're a 200‑person, PE‑backed company that's both profitable and growing. We operate as a 100% remote organization, giving our team members the flexibility to work from anywhere while making a real impact on patient care.

Learn more:

The Opportunity

You’ll be joining the founding team of Pace Mate’s brand‑new AI & Automation development group. This is a unique opportunity to shape how we leverage AI to transform remote cardiac monitoring while building automated systems that ensure data quality at scale.

As a Data Quality Automation Engineer, you’ll design and build intelligent validation systems that work across multiple project types—from small microservices to large data migration systems. You’ll also support BI projects by creating automated validation frameworks that eliminate manual testing.

What Makes This Role Different AI-First Approach

We don’t just test data—we build automated systems that use AI to validate, monitor, and improve data quality continuously. You’ll leverage Claude CLI, agentic development frameworks, and modern AI tools throughout your development workflow, not as an afterthought.

Automation Over Manual Testing

Your success is measured by how much manual validation you eliminate, not how many test cases you execute. We’re looking for someone who instinctively asks “how can I automate this?” rather than “how do I test this manually?”

Diverse Project Landscape

• Data migration systems ensuring accuracy during large‑scale transfers
• Microservices validating data flows between services
• BI projects creating automated validation for analytics and reporting
• AI/ML pipelines ensuring training data quality and model outputs

What You’ll Do Build Automated Validation Systems (70%)

• Design and implement automated data validation frameworks that scale across projects
• Develop AI‑powered quality checks that learn from patterns and anomalies
• Create programmatic tests that replace manual validation workflows
• Build monitoring systems that detect data issues before they impact patients or operations
• Write Python, PHP, and SQL scripts that validate complex data relationships automatically

Manual Validation & Root Cause Analysis (20%)

• Investigate complex data discrepancies that automated systems flag
• Perform targeted manual validation when building new automation
• Conduct root cause analysis and work with engineering teams to resolve systemic issues
• Validate data quality during critical migrations and system changes

Collaboration & Continuous Improvement (10%)

• Partner with developers, ML engineers, and product teams to understand data requirements
• Recommend enhancements to data pipelines and validation processes
• Document data quality standards and automation patterns
• Share knowledge about AI tools and automation approaches with the team

What We’re Looking For Core Skills (Non‑Negotiable) 1. Curiosity Above All

• You’re the person who asks “why does this data look this way?” and digs until you understand
• You stay current with AI advancements and experiment with new tools
• You want to understand the cardiac monitoring domain, not just test data blindly

2. AI/Agentic Development Experience

• Hands‑on experience with Claude CLI, Cursor, or similar agentic development tools
• You’ve used AI to enhance your development workflow including agents and instruction files—not just as a chatbot
• Understanding of prompt engineering and when/how to leverage LLMs effectively
• Comfortable learning and adopting new AI tools quickly

3. Solution‑Oriented Mindset

• You find ways forward rather than reasons why something can’t be done
• When you encounter a problem, you prototype solutions and iterate
• You’re resourceful and can work through ambiguity independently

4. Data‑Driven Thinking

• Deep understanding of data structures,…