Site Reliability Engineer - ED&A - Global Industrial

The Site Reliability Engineer - EDA is a technical contributor responsible for Site Reliability Engineering practices supporting the Enterprise Data & Analytics platform. This role performs reliability and operational engineering for data and analytics platforms, integrations, pipelines, and related services, including platforms such as Google Big Query, Microsoft Fabric, Power BI, Power Platform, and associated Azure and Google Cloud services. The Site Reliability Engineer establishes service-level indicators, service-level objectives, error budgets, monitoring, alerting, dashboards, and runbooks while driving incident response, root-cause analysis, automation, capacity planning, performance tuning, resilience, and production readiness.

This role provides technical guidance, engineering standards, and operational best practices for engineers supporting EDA services, and partners closely with data engineering, application, cloud, security, and governance teams to improve reliability, supportability, and business outcomes.

Performs reliability and operational engineering for the Enterprise Data & Analytics platform, including Google Big Query, Microsoft Fabric, Power BI, Power Platform, associated data pipelines, integrations, automation, reporting services, and dependent cloud services. Establishes and governs service-level indicators, service-level objectives, error budgets, availability targets, performance baselines, monitoring standards, alerting practices, dashboards, runbooks, and operational health metrics for EDA services. Analyzes telemetry from monitoring, logging, tracing, platform administration, pipeline execution, query performance, capacity, consumption, and cost-management tools to identify reliability, performance, security scalability, and efficiency improvements.

Partners with data engineering, application, cloud, security, governance, analytics, and infrastructure teams to improve platform design, integration patterns, deployment practices, release readiness, supportability, resilience, and production operations. Drives automation for provisioning, deployment, remediation, monitoring configuration, environment validation, job and pipeline health checks, alert enrichment, access reviews, incident response workflows, and operational reporting. Guides capacity planning, performance tuning, resilience engineering, disaster recovery planning, backup and restore validation, service continuity planning, architecture reviews, and production readiness assessments for EDA services.

Troubleshoots and resolves incidents involving data and analytics platforms, workloads, integrations, pipelines, APIs, automation flows, connectors, work spaces, gateways, permissions, queries, semantic models, and platform dependencies. Drives incident response, root-cause analysis, post-incident reviews, corrective actions, and reliability improvement plans to reduce recurrence, improve operational maturity, and strengthen customer experience. Provides technical guidance, engineering standards, implementation patterns, peer support, operational reviews, documentation practices, and reliability expectations for engineers supporting the EDA platform.

Promotes secure, compliant, cost-effective, and well-governed data and analytics operations by supporting access controls, data protection practices, platform governance, resource utilization reviews, lifecycle management, and operational reporting.

Typically requires a bachelor's degree and five (5) to seven (7) years of experience in a technology and/or software engineering role or an equivalent combination.

Advanced understanding of SRE principles, including reliability engineering, observability, automation, incident response, root-cause analysis, post-incident improvement, service-level indicators, service-level objectives, error budgets, and production readiness. Experience performing reliability, operations, or engineering support for enterprise data and analytics platforms used for reporting, data engineering, integration, automation, business intelligence, and business productivity workloads. Experience with Google Cloud data services such as Big Query, Cloud Storage, Cloud Logging, Cloud Monitoring, IAM, networking concepts, and workload or job performance troubleshooting.

Experience with Microsoft Azure services and operational capabilities, including Azure Monitor, Log Analytics,…