Data Quality Automation Engineer

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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.

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)

• AI/Agentic Development Experience (Claude CLI, Cursor, or similar)
• Solution-Oriented Mindset
• Data-Driven Thinking
• Programming & Automation Skills (Python, PHP, SQL)

Experience Level

• 3-5 years in software development, QA automation, or data engineering roles where you've built automated testing or validation systems. We value demonstrated ability with AI tools and automation mindset over years of traditional QA experience.

Remote Work Success

• Communicate proactively and clearly in writing
• Self-manage and stay productive without direct oversight
• Collaborate effectively across time zones
• Build relationships and contribute to culture remotely

Technical Environment

Languages & Databases

• Postgre
  
  SQL, MySQL
• Python, PHP, Java Script
• SQL for complex data validation queries

AI/ML Tools

• Claude CLI and agentic development frameworks (required)
• Experience with AI-powered code generation and testing tools
• Familiarity with prompt engineering

Bonus Experience (Preferred, Not Required)

• Databricks, Snowflake
• Data quality frameworks (Great Expectations, dbt tests)
• Cloud platforms (AWS, Azure, GCP)
• Healthcare data standards (HL7, FHIR)
• Experience with medical device data

What Success…