Vytalize Health - Data Reliability Engineer

• Education • 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. • 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. • 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.