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Role - Data Platform Observability & Monitoring Engineer Role Purpose - The Data Platform Observability & Monitoring Engineer will design, implement and operate the monitoring and observability capabilities for enterprise data and AI services. The role requires a strong data-engineering foundation and practical experience with Azure and enterprise observability platforms. The engineer will establish actionable telemetry across Azure Databricks, Azure Data Factory, Azure AI services, data pipelines and supporting infrastructure, enabling proactive detection, faster incident resolution, service-level reporting and cost-aware platform operations. Key Responsibilities - Design and implement an enterprise observability architecture for data pipelines, Databricks workloads, ADF orchestration, AI services and supporting Azure infrastructure. Onboard and standardize logs, metrics, traces and operational events from Azure services into approved monitoring and analytics platforms. Configure and manage Event Hubs, diagnostic settings, Log Analytics workspaces, Application Insights, Azure Monitor and enterprise log-ingestion routes. Develop and maintain dashboards, alerts, service-health views and management reporting for availability, failures, latency, throughput, data freshness and resource consumption. Administer or support enterprise observability platforms such as Splunk, including indexes, source types, data inputs, retention, role access, dashboards and alerts. Define telemetry standards for data pipelines, including correlation IDs, run IDs, project metadata, error classification, business context and data-quality outcomes. Work with data engineers to embed structured logging, metrics and traceability into Azure Databricks, Azure Data Factory and streaming/batch solutions. Create monitoring patterns for end-to-end lineage and operational correlation across source systems, orchestration, processing, storage and downstream consumption. Investigate incidents using logs and telemetry, perform root-cause analysis and recommend preventive engineering improvements. Define and report service-level indicators and objectives for critical data and AI services. Implement monitoring-as-code and automated deployment of alerts, dashboards, diagnostic settings and observability configuration. Monitor ingestion volume, retention and platform usage to maintain observability coverage while controlling cost. Support security, audit and compliance requirements by ensuring appropriate log coverage, retention, access controls and evidence. Produce runbooks, support documentation and training for engineering and operational teams. Required Experience and Skills - 5+ years of experience in data engineering, cloud engineering, platform engineering or a closely related technical role. Strong data-engineering knowledge, including data pipelines, orchestration, batch/stream processing, data quality and production support concepts. Hands-on experience with at least one enterprise observability platform such as Splunk, Azure Monitor, Datadog, Dynatrace, Grafana/Prometheus or Elastic. Practical Azure experience, including diagnostic settings, Log Analytics, Application Insights, Event Hubs, storage and identity/access concepts. Experience designing actionable dashboards and alerts rather than collecting logs without defined operational use cases. Strong SQL skills and working scripting/development skills in Python, PowerShell or a comparable language. Experience investigating distributed-system incidents and performing structured root-cause analysis. Understanding of logs, metrics and traces, including telemetry quality, cardinality, retention, sampling and correlation. Knowledge of secure access, data sensitivity, auditability and least-privilege controls for monitoring platforms. Strong written and verbal communication skills and the ability to work with engineering, operations, security and management stakeholders. Preferred / Good to Have - Hands-on experience monitoring Azure Databricks, Unity Catalog, Spark workloads and Azure Data Factory pipelines. Splunk administration experience, including index design, source types, role-based access, dashboards, alerts and ingestion troubleshooting. Experience with OpenTelemetry and distributed tracing across applications, agents, APIs and data pipelines. Experience with Azure AI Foundry, machine-learning platforms, GenAI applications or AI-agent observability. Experience with infrastructure-as-code and CI/CD for monitoring configuration, such as Terraform, Azure DevOps or Git-based deployment. Knowledge of FinOps monitoring, consumption analytics and cost optimisation for A .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.