Senior Software Engineer

Design, implement, and operate telemetry collection and processing services across Dataverse, Service Fabric, Kubernetes, and Finance and Operations. Partner with teams building Copilot and agent workloads to make their platform dependencies observable. Own data quality and pipeline behavior: event schemas, filtering, transformations, routing, ingestion latency, completeness, and recovery from delayed or failed delivery. Make delivery guarantees and failure modes explicit. Build resilience through bounded buffering, backpressure, retries, circuit breakers, and dependency isolation. Validate failure recovery and capacity through automated tests, load testing, and controlled fault injection, without putting source workload availability at risk. Enforce privacy, security, and data residency throughout collection, routing, storage, retention, and access. Build least-privilege controls and auditable operations that respect regional, EU Data Boundary, and sovereign cloud requirements. Improve Azure Data Explorer ingestion and query performance, storage efficiency, and capacity management. Use evidence to balance telemetry coverage and retention against compute, storage, and operational cost, and automate safe deployments and configuration changes. Instrument the telemetry platform itself with actionable health signals and alerts. Drive incident mitigation, root-cause analysis, and lasting repairs; improve the service-health measurements and diagnostic tools that partner teams use to understand customer impact. Partner with service teams on instrumentation, onboarding, query design, and operational readiness. Build self-service tools and documentation, mentor engineers, and raise the engineering bar through design and code review. Build and improve AI-assisted diagnostics and Model Context Protocol (MCP) integrations that help engineers discover telemetry, generate useful queries, and correlate evidence across services. Evaluate their accuracy and usefulness, respect data-access boundaries, and keep production-changing actions subject to appropriate safe Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python These requirements include but are not limited to the following specialized security screenings: Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Solid software engineering fundamentals, including data structures, concurrency, asynchronous processing, automated testing, and diagnosing performance and reliability problems in production. Practical experience with distributed data processing or telemetry pipelines, including ingestion, buffering, retries, partitioning, data quality, and the tradeoffs between throughput, latency, and cost. Production experience in a modern programming language such as C#, Go, Java, or C++, and the ability to learn the languages and tools used by the team. Demonstrated experience making services resilient under stress, validating failure and recovery behavior, and using logs, metrics, and queries to explain system behavior. Experience operating what you build, including on-call ownership, live-site debugging, and post-incident improvement. Hands-on experience with Azure Data Explorer and KQL, including ingestion, query optimization, retention policies, and managing shared analytics capacity. Experience with observability technologies such as Geneva, OpenTelemetry, Prometheus, or comparable collection, metrics, and monitoring platforms. Experience operating services on Kubernetes or Service Fabric, building stream-processing or data lake pipelines, and automating infrastructure and deployments in Azure. Familiarity with multi-tenant cloud platforms, privacy-aware data handling, regional data residency, or sovereign cloud operations. Background in capacity planning, telemetry cost optimization, or measuring service health and customer impact in large-scale cloud systems. Experience building AI-assisted developer tools, MCP integrations, or incident investigation automation, including evaluating output quality and applying appropriate access controls and operational safeguards.