Data and AI Quality Automation Engineer

Job Overview:

The Data and AI Automation Engineer designs and builds automated systems to ensure the accuracy, completeness, and reliability of data across Stratus’s clinical, operational, and AI-driven platforms. This role is central to delivering trusted data for analytics and decision-making within a HIPAA-regulated healthcare environment.

This job combines data engineering, quality assurance, and automation to focus on using automation to replace manual checks with scalable systems, real-time monitoring, and built-in quality controls throughout data pipelines. Engineering partners across all departments—including IT, clinical operations, business functions, and data engineering—to proactively detect issues, address root causes, and ensure data quality is embedded at every stage of the data lifecycle.

This position also supports data governance and compliance by aligning data quality practices with HIPAA and SOC 2 requirements, ensuring solutions are secure, auditable, and compliant by design.

Key Responsibilities:

Data & AI Opportunity Discovery and Execution Conduct structured listening tours across all departments (clinical, operations, finance, IT, etc.) to identify data quality gaps, manual workflows, and AI automation opportunities

Map end-to-end data flows, dependencies, and failure points across systems (migration, microservices, BI, AI/ML pipelines)
Perform gap analysis and impact assessment, prioritizing initiatives based on risk, operational impact, and scalability

Translate business and clinical needs into clear technical requirements, validation strategies, and automation roadmaps

Own the full lifecycle from discovery design executionmonitoring, ensuring solutions deliver measurable outcomes

Partner with stakeholders to align priorities, success metrics, and adoption of automated and AI-driven solutions.

Automated Validation System Development Design and implement automated data validation frameworks that scale across migration, microservice, BI, and AI/ML project types.

Develop AI-powered quality checks that learn from data patterns and surface anomalies before they reach clinical or operational systems.

Build programmatic tests and monitoring pipelines that replace manual validation workflows end-to-end.

Write Python and SQL scripts that validate complex data relationships, referential integrity, and business rules automatically.

Maintain and extend validation libraries so that new projects inherit proven quality checks from day one.

Manual Validation & Root Cause Analysis Investigate complex data discrepancies surfaced by automated systems — dig into root cause, not just symptoms.

Perform targeted manual validation when building new automation or validating critical system migrations.

Partner with engineering and clinical teams to resolve systemic data quality issues and prevent recurrence.

Validate data accuracy and completeness during high-stakes migrations and platform changes.

AI-Assisted & Autonomous Development Leverage agentic AI development tools (e.g., Claude, Cursor) throughout the development lifecycle — not as a novelty, but as a core productivity and quality practice.

Apply prompt engineering techniques to accelerate validation script development, anomaly analysis, and documentation.

Stay current on AI tooling advances and proactively propose where new tools can improve data quality outcomes.

Collaboration & Continuous Improvement Partners across all departments align data requirements and ensure quality standards are proactively embedded upstream within systems and workflows.

Recommend and implement enhancements to data pipelines, validation processes, and quality monitoring dashboards.

Document data quality standards, validation patterns, and automation runbooks for team-wide use.

Contribute to Stratus's data governance practices, including alignment with HIPAA data integrity requirements.

Learning & Development Continuously develop expertise in data engineering, AI tooling, and healthcare data standards.

Stay current on emerging validation frameworks, data quality tools, and automation best practices.

Education & Experience Bachelor’s degree in computer science, Information Systems,…