Microsoft Principal Data Engineer Supply Chain 

United States, Washington 

802577780

Required/Minimum Qualifications

• Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related fieldAND 6+ 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 fieldAND 4+ years experience in business analytics, data science, software development, or data engineering work
  • OR equivalent experience.
• 3+ years designing data models for operations and supply chains in a distributed data environment.
• 3+ years of writing SQL/KQL, programming languages (e.g., Python, Java), and data transformation tools. Experience with cloud platforms (e.g., Azure, AWS) and big data technologies (e.g., Hadoop, Spark).

Background Check 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.

Preferred Qualifications:

• Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related fieldAND 12+ 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 fieldAND 8+ years of business analytics, data science, software development, data modeling or data engineering work experience
  • OR equivalent experience.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:Microsoft will accept applications for the role until January 30, 2025.

• Data Governance : Ensure data modeling and handling comply with laws and policies. Develop processes to document data type, classifications, and lineage for traceability. Govern data accessibility across pipelines and oversee data glossaries.
• Data Tools Optimization : Identify opportunities to optimize data tools for transforming, managing, and accessing data. Write code to test data platforms and implement sustainable design patterns. Identify trends to inform future data architecture designs.
• Data Extraction and Validation : Extract raw data from multiple sources using query languages, tools, or machine learning algorithms. Ensure data accuracy, validity, and reliability. Contribute to code reviews and drive the business case for advanced orchestration techniques. Plan and strategize data protocols and aggregation approaches to validate data quality.
• Data Transformation : Develop and use advanced techniques to transform raw data into compatible formats for downstream sources. Expand the application and reusability of software and tools. Drive efficiencies in data extraction to ensure quality and completeness.
• Stakeholder Collaboration : Collaborate with stakeholders to recommend data requirements. Partner with business teams to determine data costs, access, usage, and availability. Negotiate agreements with partners and system owners for project delivery and data ownership.
• Data Modeling : Design data models that meet business requirements and translate business needs into design specifications. Lead conversations with stakeholders to improve data models and schemas. Develop solutions considering analytical requirements and compute/storage consumption.
• Root Cause Analysis : Conduct root cause analysis for detected problems and implement solutions to prevent recurrence. Monitor self-healing processes to maintain data quality and performance. Use cost analysis to drive solutions and reduce budgetary risks.
• Performance Monitoring : Ensure effective performance monitoring across data pipelines. Build automation into data visualizations and aggregations to monitor data quality and pipeline health. Develop troubleshooting guides and operating procedures for addressing complex problems flagged by automated testing.