January 18, 2025

Data Engineer II

Mid • Hybrid • On-site

$98,300 - $208,800/yr

Redmond, WA

Overview

The Security Customer Experience (CxE) organization is a part of the Security Engineering Division. We work with customers and partners from all over the world to drive service adoption to Microsoft Security products.

 

We have an exciting opportunity for a Data Engineer II to work in the Shared Services team within the CxE organization, one of the most customer-connected engineering teams at Microsoft. We are looking for a customer-focussed individual, with a growth mindset, compassion for customer experience, and an experimental approach to join our team.

 

The Shared Services team is a group of geo-distributed Product Managers, Data Engineers and Data Scientists building a deep understanding of the business and customers, conducting opportunity analysis, defining product metrics, and generating key insights to drive business decisions. As a Data Engineer in the team, you will be responsible for managing the data real estate, architecting, and developing the next-gen data platform as well as conducting deep data analysis.

 

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Qualifications

Required/Minimum Qualifications

  • Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering , or related field AND 2+ years experience in business analytics, data science, software development, data modeling or data engineering work
    • OR Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering or related field AND 1+ year(s) experience in business analytics, data science, software development, or data engineering work
    • OR equivalent experience.
  • 2+ years experience with data pipelining and transformation technologies

Other Requirements: 

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. 

 

Additional or Preferred Qualifications

  • Bachelor's Degree in Computer Science , Math, Software Engineering, Computer Engineering , or related field AND 5+ years experience in business analytics, data science, software development, data modeling or data engineering work
    • OR Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering , or related field AND 3+ years of business analytics, data science, software development, data modeling or data engineering work experience
    • OR equivalent experience.
  • Hands-on experience with big data technologies as well as data analytics tools
  • Previous data visualization skills to be able to present insights that drive business impact
  • Communication & collaboration skills  
  • Track record of self-directed execution
  • Knowledge of Machine Learning/Predictive modelling is a significant plus

Data Engineering IC3 - The typical base pay range for this role across the U.S. is USD $98,300 - $193,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $127,200 - $208,800 per year.

   

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

    

Microsoft will accept applications for the role until January 22, 2025   

 

 

#MSFTSECURITY 

Responsibilities

  • API integration
  • ETL (Extract, Transform, Load) data processing
  • Feedback tracking
  • Pre-processing and processing for AI pipelines
  • Setting up scalable data ingestion
  • Ensure seamless data flow for the platform