Data and AI Quality Automation Engineer

Stratus•Irving, TX

4d•Onsite

About The Position

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.

Requirements

Bachelor’s degree in computer science, Information Systems, Data Engineering, or a related field.
Minimum of five (5) years of experience in software development, data engineering, QA automation, or a closely related technical role.
Demonstrated experience building automated testing or data validation systems — not just executing test cases.
5+ years of hands-on experience building automated data validation, QA automation, or data engineering pipelines.
Strong proficiency in C#, Python — able to write production-quality validation scripts, not just ad-hoc automation.
Strong SQL skills — able to write complex queries validating referential integrity, data relationships, and business logic across relational databases (MSSQL, MySQL, or equivalent).
Solid understanding of: Data structures, schemas, and dependency relationships across multi-system environments
Solid understanding of: Data pipeline architecture and where quality controls must be embedded
Solid understanding of: Root cause analysis methodologies for complex data discrepancies
Hands-on experience with AI-assisted development tools (e.g., Claude, Cursor, or equivalent agentic development frameworks) used meaningfully in a professional workflow, not just experimentally.
Automation-first mindset — the instinct is always to build a system, not execute a manual check.
Clear written and verbal communication skills, including the ability to document technical standards for cross-functional audiences.
Ability to work independently, manage priorities without direct oversight, and communicate proactively with distributed teams.
(Equivalent combination of education and directly demonstrated experience will be considered.)
Ability to sit for extended periods of time.
Repetitive movement of fingers and hands
Talking and hearing
Reaching with hands and arms
Clarity of vision at 20 feet or less
Read, evaluate and interpret data.
Performing Data entry mathematical operations

Nice To Haves

Prior experience working with healthcare, clinical, or other regulated data environments preferred.
Familiarity with data quality frameworks such as Great Expectations or dbt Tests.
Experience with cloud data platforms: Databricks, Snowflake, AWS, Azure, or GCP.
Experience with real-time data streaming (Kafka, Event Hub)
Knowledge of healthcare data standards: HL7, FHIR, or medical device data formats.
Experience with front-end or API testing tools (Puppeteer, Playwright, Postman).
Familiarity with JavaScript for web application data validation.
Exposure to AI/ML pipeline data quality practices — training data validation, model output monitoring.
Experience in a SOC 2–certified or HIPAA-regulated technology environment.
Insatiable curiosity — you ask, "why does this data look this way?" and dig until you understand.
Solution-oriented: you prototype and iterate rather than cataloguing reasons something can't be done.
Strong analytical and problem-solving skills with a high tolerance for data ambiguity.
Collaborative mindset — able to work across IT, clinical operations, data engineering, and business units.
Detail-oriented with a proactive approach to surfacing data quality issues before they become incidents.

Responsibilities

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 → execution → monitoring, ensuring solutions deliver measurable outcomes
Partner with stakeholders to align priorities, success metrics, and adoption of automated and AI-driven solutions.
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.
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.
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.
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.
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.