Senior Software Engineer

Content Service Metrics and Analysis Team is responsible for global msn (https://www.msn.cn/zh-cn, Microsoft content service) business growth by building metrics to evaluate gain and quality, driving insights from big data (400TB per day) and models,  providing an AB Experimentation System built on big data with computations on platforms like spark, click house, cosmos, and building an E2E monitoring system on metrics with common auto analysis functions like abnormal detection, prediction, metrics movement, correlation and etc. You will also have a chance to use LLM for relevance on both prompt and embedding. Overall, we provide an E2E service to facilitate msn business growth based on data, models, insights, monitoring and experiments. The role for this job is mainly on big data pipelines for data cooking and system development on AB Experiment System according to business requirements.

1. Bachelor's Degree
2. 4+ year work experience in the area of system development or big data processing

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

1.  Understand the business from data, metrics and system. 
2. Build data pipelines on multiple computation platforms including spark, kusto, cosmos, databricks and etc for reporting, analysis and metrics pre-computation with stable SLA.
3. Build pipeline monitor system to prevent from data missing and data process issue. Also, design and setup the quality gate for data quality is a must in the work. 
4. Build Eco-System for A/B for on-demand metrics computation and experiment analysis. In this system, we will auto generate click house query for quick computation and we will auto listen the experiment scorecard for auto analysis.
5. Good coding senses independent of the language you are using in the development as we need different languages for different system. For example, we use scala for spark, C#+sql for cosmos and pyspark for databricks.
6. Drive engineering excellence for lower cost, higher efficiency, better robust of the system, quicker data processing and etc.