Data Reliability Engineer

Data Reliability Engineer (DRE) at Vytalize Health is responsible for ensuring the end‑to‑end reliability, quality, and operational health of data across the full data lifecycle—from ingestion through downstream delivery and consumption. The role sits at the intersection of Data Engineering and Data Services, focusing on building confidence that data is accurate, timely, observable, and dependable for both internal and external consumers.

• Own and continuously improve the reliability of data pipelines across ingestion, transformation, and delivery layers, ensuring data is accurate, complete, and delivered on schedule.
• Establish and maintain data reliability standards, including Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs) for both upstream ingestion and downstream data delivery.
• Design, implement, and maintain comprehensive monitoring, logging, and observability frameworks for data pipelines, datasets, and data services with clear visibility into freshness, volume, schema changes, and data quality.
• Design and implement data quality testing and validation frameworks—establishing test cases, golden datasets, and regression tests to detect quality issues early.
• Establish data quality metrics and KPIs; measure and track data accuracy, completeness, timeliness, and consistency across pipelines.
• Lead incident response for data reliability issues, including detection, triage, communication, root cause analysis, and post‑incident remediation with documented corrective actions.
• Drive improvements in pipeline resiliency through retry strategies, backfills, idempotency, schema enforcement, and safe deployment practices.

• 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, Git Hub 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 Dev Ops 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.

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

Experience

• 5+ years of experience working with data platforms,…