Senior Software Engineer

Microsoft•Redmond, WA

1d•$119,800 - $234,700

About The Position

Microsoft's Azure Data engineering team is leading the transformation of analytics in the world of data with products like databases, data integration, big data analytics, messaging & real-time analytics, and business intelligence. The products in our portfolio include Microsoft Fabric, Azure SQL DB, Azure Cosmos DB, Azure PostgreSQL, Azure Data Factory, Azure Synapse Analytics, Azure Service Bus, Azure Event Grid, and Power BI. Our mission is to build the data platform for the age of AI, powering a new class of data-first applications and driving a data culture. Within Azure Data, the big data analytics team provides a range of products that enable data engineers and data scientists to extract intelligence from all data – structured, semi-structured, and unstructured. We build the Data Engineering, Data Science, and Data Integration pillars of Microsoft Fabric. The Fabric Data Engineering Experience & Infrastructure team is looking for a passionate engineer to help build the next generation of infrastructure services for Microsoft Fabric Data Engineering, powered by Apache Spark. If you enjoy working on high-scale distributed systems, developer experiences, and platform infrastructure that makes engineers more productive, this is a great place to do it.

Requirements

Strong software engineering fundamentals and experience shipping production services
Comfort working with distributed systems and performance-critical code
Experience with Spark and/or big data systems is a big plus (but not required if you’re eager to learn)
Collaborative mindset and ownership mentality—someone who likes building, iterating, and improving systems over time
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 OR equivalent experience .
Strong software engineering fundamentals (data structures, algorithms, testing, debugging, performance).
Experience building and shipping production infrastructure (backend services, distributed systems, or platform components) in a cloud environment.
Solid understanding of distributed systems concepts: fault tolerance, scaling, scheduling, and resource management.
Proficiency in one or more backend/system languages (e.g., Java, Scala, C#, C++, or Python).
Quick learner with strong growth mindset—able to ramp up rapidly in new domains, tools, and codebases.
Ability to thrive in an AI-powered engineering environment: comfortable adopting AI-assisted workflows (e.g., copilots/agents), iterating quickly, and continuously improving productivity and quality .
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

Experience designing and operating large-scale infrastructure for data platforms or compute services (e.g., job orchestration, runtime services, cluster/resource management, multi-tenant systems).
Experience with observability and operational excellence (SLOs/SLIs, alerting, incident response, postmortems).
Performance and reliability engineering experience (profiling, optimization, capacity planning, cost/performance tradeoffs).
Familiarity with modern cloud-native patterns (service ownership, CI/CD, safe deployments, automation).

Responsibilities

Build and operate core infrastructure services that power Fabric Data Engineering on Spark
Improve scalability, resiliency, and observability across Spark-based services
Partner closely with product, client/UX, and runtime teams to ship end-to-end experiences
Drive engineering excellence through design reviews, testing, incident learnings, and performance tuning
Intelligent job/session orchestration and scheduling improvements
Runtime performance optimizations (caching, adaptive execution, cost/perf tuning)
Debuggability & observability (logs/metrics/traces, diagnostics experiences)
Reliability tooling (auto-heal, safe rollouts, incident reduction)
Data engineering developer experience improvements (config, templates, integrations)
Design and develop world-class experiences for a new big data cloud offering, with an emphasis on scale, reliability, and performance.
Build and evolve core infrastructure services that power data engineering and analytics workloads (compute, runtime services, job/session management, configuration, and platform integrations).
Drive technical design and implementation end-to-end: translate requirements and documentation into robust production code.
Troubleshoot and improve systems using source code analysis and production instrumentation (logs, metrics, traces), and turn operational learnings into engineering improvements.
Improve platform scalability, resiliency, and observability, including automation to reduce operational toil.
Partner closely with product and engineering teams to deliver end-to-end features and continuously raise the quality bar.
Embody our culture and values