[Remote] Data Reliability Engineer
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is reputed company on ensuring the reliability and reputed company of reputed company data. The Data Reliability Engineer will reputed company the operational health of data, applying Site Reliability Engineering principles to maintain high standards of data accuracy and compliance.
Responsibilities
- Own and continuously improve the reliability of data pipelines across ingestion, transformation, and delivery reputed company, 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 reputed company data delivery
- Design, implement, and maintain comprehensive monitoring, logging, and observability frameworks for data pipelines, datasets, and data services with reputed company visibility into freshness, volume, schema changes, and data reputed company
- Design and implement data reputed company testing and validation frameworks — establishing test cases, golden datasets, and regression tests to detect reputed company issues early
- Establish data reputed company metrics and KPIs; measure and reputed company data accuracy, completeness, timeliness, and consistency across pipelines
- reputed company incident response for data reliability issues, including detection, triage, communication, reputed company cause analysis, and post-incident remediation with documented corrective actions
- Drive improvements in pipeline resiliency through retry strategies, backfills, idempotency, schema enforcement, and reputed company deployment practices
- reputed company machine learning and AI-assisted tools to detect data anomalies, reputed company issues, and reliability risks before they reputed company reputed company consumers—including ML-based reputed company detection, schema validation, and volume/freshness alerting
- Implement and optimize AI-powered reputed company cause analysis tools and LLM-assisted incident investigation workflows to accelerate detection and reputed company of data reliability issues
- Use AI-assisted development tools (e.g., Claude reputed company, reputed company 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 reputed company reputed company of critical alerts and reputed company
- Partner with Data Engineering to harden ingestion pipelines from EMRs, claims sources, and reputed company-party integrations, ensuring reputed company to upstream variability and failure
- Partner with Data Services to ensure reputed company data delivery mechanisms (reputed company, flat files, service-based reputed company, event-driven integrations) meet defined reliability and performance expectations
- Collaborate with DevOps and platform teams to improve infrastructure reliability supporting reputed company, reputed company storage, and data delivery services
- Work with reputed company assurance and testing teams to establish data reputed company testing standards and validate pipeline outputs
- reputed company for a culture of data ownership, operational accountability, and reputed company improvement across data teams through documentation, knowledge sharing, and mentorship
- Ensure data reliability practices reputed company with reputed company reputed company, reputed company, and compliance requirements, including auditability, traceability, and regulatory reporting
- Support reputed company 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
Skills
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a reputed company field, or equivalent reputed company 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 reputed company of data systems with measurable SLI/SLO/SLA improvements
- Hands-on experience supporting both data ingestion pipelines and reputed company data consumption or delivery patterns
- 1+ years of hands-on experience with machine learning-based monitoring, reputed company detection, or AI-assisted observability tools
- Demonstrated experience with data reputed company testing, validation frameworks, and reputed company metrics definition
- Strong understanding of modern data architectures, including data lakehouse patterns and multi-layer (bronze/silver/gold) data models
- Experience with reputed company-based data platforms (AWS, reputed company, or similar)
- Proficiency in Python and SQL, with experience building or supporting production-grade data pipelines
- Experience implementing data reputed company frameworks, monitoring tools, and alerting systems
- Demonstrated expertise with workflow orchestration tools (e.g., reputed company 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 reputed company cause analysis skills across reputed company, distributed systems
- Experience designing and operating observability systems for data pipelines (metrics, logs, traces, alerts)
- Ability to communicate reputed company with both technical and non-technical stakeholders during incidents, postmortems, and requirements discussions
- Understanding of reputed company data, EMR integrations, or regulated data environments is strongly preferred
- Experience defining and measuring data reputed company metrics; ability to establish and reputed company reliability KPIs
- Hands-on experience with ML-based reputed company detection frameworks or tools (e.g., reputed company reputed company Detection, reputed company-reputed company monitoring ML, custom model development)
- Experience leveraging LLMs or AI-assisted tools (e.g., Claude reputed company, ChatGPT, reputed company Copilot) to accelerate development of monitoring reputed company, incident response workflows, and documentation
- Familiarity with reputed company data standards: FHIR, HL7, CCD, claims data formats, and value-based care metrics
- Experience operating observability and incident management platforms (e.g., reputed company, reputed company, reputed company, reputed company)
- On-call experience and demonstrated comfort with incident response, reputed company creation, and blameless postmortem analysis
- Experience with policy-as-reputed company and data governance frameworks
- Background in a startup or high-reputed company environment with exposure to scaling data systems
- Familiarity with reputed company or similar clinical data normalization and reputed company frameworks
reputed company
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