Site Reliability Engineer

Microsoft is a company where passionate innovators come to collaborate, envision what can be and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit thinking in a cloud-enabled world. 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 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.

Service Reliability Team Data and how it is interpreted is essential for every business to succeed. The team is responsible for ensuring our critical services are running efficiently, securely and with high reliability. We work with many different teams to improve service reliability by continually innovating tooling, automation services and processes to make supporting our products scalable and efficient.

We do not just value differences or different perspectives. We seek them out and invite them in so we can tap into the collective power of everyone in the company. As a result, our customers are better served.

Responsibilities: • Designing and implementing Service Reliability services, tooling and processes. • Generating software specifications, proof-of-concepts, and prototype solutions given high level feature requirements. • Working closely with team members to unblock each other and share learnings and knowledge. • Using data and telemetry to improve feature work and propose feature improvements to existing products.

Embody our culture and values.