Data Engineer II

Microsoft is a leading enterprise service company focused on securing digital technology platforms and ensuring the integrity of its internal systems. They are seeking a Data Engineer II to join the Insider Risk Engineering team, where the role involves developing data pipelines and building insider risk detection models to identify potential threats. This position emphasizes collaboration, innovation, and analytical rigor to enhance security across Microsoft’s global environments.

Responsibilities:

• Design, build, and optimize data pipelines to ingest, process, and prepare data for use in insider risk detection models
• Join, filter, and integrate diverse data sources to create comprehensive datasets that enable effective and accurate insider risk detections
• Work with large datasets, applying advanced data transformation techniques to ensure data quality and accessibility for risk detection
• Develop, test, and deploy insider risk detection models based on data-driven insights to proactively identify anomalous or risky behavior patterns
• Collaborate with insider risk team members to define and refine detection use cases, ensuring they are accurate, scalable, and aligned with business needs
• Share knowledge and actively contribute ideas in team technical discussions
• Maintain and monitor insider risk engineering systems to ensure reliable operation, security, and compliance with internal engineering standards and policies
• Join on-call rotations, lead incident response, and drive thorough root-cause analysis
• Document data processes, detection workflows, and system configurations to support future development and maintenance
• Use development and coding best practices (e.g., reusable, modular)
• Own end-to-end quality for the code you deliver, including testing and DevOps automation

Requirements:

• 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, data modeling, or data engineering
• OR 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
• OR equivalent experience
• Candidates must be able to meet Microsoft, customer and/or government security screening requirements are required for this role
• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter
• Citizenship & Citizenship Verification: This role will require access to information that is controlled for export under export control regulations
• To meet this legal requirement, the successful candidate will be required to provide either proof of their country of citizenship or proof of their U.S. permanent residency or other protected status
• To meet this legal requirement, the successful candidate's citizenship will be verified with a valid passport
• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent work experience
• 4+ years of experience in data engineering, data science, or a hybrid data engineering/data science role
• Proficiency in query languages (e.g., SQL, KQL)
• Experience with object-oriented programming languages (e.g., Python, C#, Java, or C++)
• Experience with security product usage such as Insider Risk Management or Sentinel
• Demonstrated ability to build and manage data systems in the cloud
• Experience with big data systems and tools, such as PySpark, Databricks, or Azure Synapse
• Familiar with data engineering best practices like layered data architecture, data modeling, and developing reliable and scalable data pipelines
• Strong understanding of engineering and security compliance standards, with experience in regulated environments
• Excellent problem-solving skills and attention to detail, with a proactive approach to identifying and mitigating risks
• Experienced working within agile frameworks such as Scrum and Kanban