Data Reliability Engineer

Leverage machine learning and AI-assisted tools to detect data anomalies, quality issues, and reliability risks before they impact downstream consumers—including ML-based drift detection, schema validation, and volume/freshness alerting.

Implement and optimize AI-powered root cause analysis tools and LLM-assisted incident investigation workflows to accelerate detection and resolution of data reliability issues.

Use AI-assisted development tools (e.g., Claude Code, GitHub Copilot, or similar) to accelerate development of monitoring frameworks, runbooks, and incident response automation.

Establish patterns and best practices for integrating AI-driven observability into data systems while maintaining explainability and human oversight of critical alerts and decisions.

Partner with Data Engineering to harden ingestion pipelines from EMRs, claims sources, and third-party integrations, ensuring resilience to upstream variability and failure.

Partner with Data Services to ensure downstream data delivery mechanisms (APIs, flat files, service-based access, event-driven integrations) meet defined reliability and performance expectations.

Collaborate with DevOps and platform teams to improve infrastructure reliability supporting Databricks, cloud storage, and data delivery services.

Work with quality assurance and testing teams to establish data quality testing standards and validate pipeline outputs.

Advocate for a culture of data ownership, operational accountability, and continuous improvement across data teams through documentation, knowledge sharing, and mentorship.

Ensure data reliability practices align with healthcare security, privacy, and compliance requirements, including auditability, traceability, and regulatory reporting.

Support capacity planning and scaling efforts by analyzing pipeline performance, usage patterns, and failure modes to identify infrastructure and architectural improvements.

Maintain comprehensive documentation of reliability standards, SLAs, incident runbooks, and observability architecture for both technical and non-technical stakeholders.

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent professional experience.

5+ years of experience working with data platforms, data pipelines, or distributed data systems in production environments.

Demonstrated experience improving reliability, observability, or operational quality of data systems with measurable SLI/SLO/SLA improvements.

Hands-on experience supporting both data ingestion pipelines and downstream data consumption or delivery patterns.

1+ years of hands-on experience with machine learning-based monitoring, anomaly detection, or AI-assisted observability tools.

Demonstrated experience with data quality testing, validation frameworks, and quality metrics definition.