Software Engineer

Microsoft•Vancouver, BC

1d•CA$85,800 - CA$169,300

About The Position

As a Software Engineer in the Cloud + AI organization, you will have the opportunity to work on Microsoft’s Azure Data engineering team, which is leading the transformation of analytics in data with pioneering products such as Microsoft Fabric, Azure SQL Database, Cosmos Database, PostgreSQL, Azure Data Factory, Azure Synapse Analytics, Azure Service Bus, Azure Event Grid, and Power BI. Our mission is to create data platforms for the artificial intelligence era—powering a new generation of data-first applications and driving a data-centric culture. The Microsoft Fabric platform team builds and maintains the foundational operating system that provides customers with a unified data stack, unified experience, unified governance, business model, and architecture. Here, your contributions will directly enable customers to realize the full potential of their data and shape industry-wide analytics and intelligence solutions. You will integrate best-in-class Azure technologies to achieve reliability at scale, design and build high-quality components, and help transform how businesses harness the power of data and artificial intelligence. If you are passionate about enabling a robust data estate for the next generation of applications and want to contribute to a unified platform that empowers customers and partners to achieve more, we invite you to join our team. At Microsoft, our mission to empower every person and every organization on the planet to achieve more guides how we partner with customers to deliver trusted, impactful solutions. With a growth‑mindset culture, we innovate responsibly and measure success by shared progress, people, teams, and customers. Join us to do meaningful work that changes the world and helps shape what’s next for everyone.

Requirements

Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Nice To Haves

Master's Degree in Computer Science or related technical field AND 3+ 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 5+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Responsibilities

Contribute to the identification of requirements for, and development of automation within production and deployment of a complex product feature, targeting zero-touch deployment where possible.
Run code in simulated or other non-production environments to confirm functionality and error-free runtime for products with little to no oversight.
Review product feature code and test code to ensure it meets team standards, contains the correct test coverage, and is appropriate for the product feature.
Contribute to bringing insight to code reviews, helping improve code quality, and coaching and providing feedback to develop other engineers' skills with minimal guidance.
Contribute to code reviews in a timely fashion to help accelerate the pace of development on the team.
Consider diagnosability, reliability, testability, and maintainability when reviewing code.
Apply best practices to build code based on well-established methods and secure design principles while also applying best practices for new code development and formal validation of security invariants.
Follow best practices for product development and scaling to customer requirements, and meeting scaling needs, performance expectations, and security promises.
Leverage internal experimentation infrastructures to conduct experiments that determine the impact of changes, using feature flags and flighting.
Collaborate with internal partners such as data scientists and product managers to incorporate success and guardrail metrics for experimentation with minimal guidance.
Identify areas to contribute to integration of logging and instrumentation for gathering telemetry data on system behavior, such as performance, reliability, availability, usage, and safety mechanisms.
Enable monitoring and investigation of security-related concerns and scenarios for both live and A/B experiments.
Contribute to improving monitoring designs by classifying and analyzing data and creating outputs that enhance system monitoring and issue identification and mitigation, while considering the privacy implications of telemetry changes.