About this opportunity
Vytalize Health lists this Data Reliability Engineer opportunity in kansas, Ohio. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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.
Essential Functions Of The Role
Data Pipeline Reliability & Operations
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.
AI‑Powered Observability & Anomaly Detection
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.
Partnership & Standards
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.
Compliance & Operational Excellence
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.
Qualifications
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, 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.
Knowledge, Skills, And Abilities
Strong understanding of modern data architectures, including data lakehouse patterns and multi‑layer (bronze/silver/gold) data models.
Experience with cloud‑based data platforms (AWS, Databricks, or similar).
Proficiency in Python and SQL, with experience building or supporting production‑grade data pipelines.
Experience implementing data quality frameworks, monitoring tools, and alerting systems.
Demonstrated expertise with workflow orchestration tools (e.g., Databricks Workflows, Airflow) and version‑controlled deployment practices.
Familiarity with SRE and reliability engineering concepts including SLIs, SLOs, error budgets, and blameless postmortem culture.
Strong troubleshooting and root cause analysis skills across complex, distributed systems.
Experience designing and operating observability systems for data pipelines (metrics, logs, traces, alerts).
Ability to communicate clearly with both technical and non‑technical stakeholders during incidents, postmortems, and requirements discussions.
Understanding of healthcare data, EMR integrations, or regulated data environments is strongly preferred.
Experience defining and measuring data quality metrics; ability to establish and track reliability KPIs.
Preferred Qualifications
Hands‑on experience with ML‑based anomaly detection frameworks or tools (e.g., Datadog Anomaly Detection, cloud‑native monitoring ML, custom model development).
Experience leveraging LLMs or AI‑assisted tools (e.g., Claude Code, ChatGPT, GitHub Copilot) to accelerate development of monitoring code, incident response workflows, and documentation.
Familiarity with healthcare data standards: FHIR, HL7, CCD, claims data formats, and value‑based care metrics.
Experience operating observability and incident management platforms (e.g., DataDog, New Relic, Sumo Logic, PagerDuty).
On‑call experience and demonstrated comfort with incident response, runbook creation, and blameless postmortem analysis.
Experience with policy‑as‑code and data governance frameworks.
Background in a startup or high‑growth environment with exposure to scaling data systems.
Familiarity with Tuva or similar clinical data normalization and quality frameworks.
This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee. Other duties, responsibilities, and activities may change or be assigned at any time with or without notice.
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Worksite address
kansas, OH, 44841, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.