> Markdown version of [/jobs/ext/4702744-data-engineer](https://www.wearedevelopers.com/jobs/ext/4702744-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Microsoft - **Location:** San Francisco, CA, United States - **Salary:** $102,100 - $202,200 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Microsoft Online Services, Software Quality, Code Review, Computer Engineering, Continuous Integration, Customer Data Management, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Transformation, Data Security, Data Systems, Data Warehousing, DevOps, Dimensional Modeling, Distributed Computing Environment, Distributed Data Store, Operational Databases, Performance Tuning, Power BI, Software Engineering, SQL Databases, Data Processing, Apache Spark, Git, Microsoft Fabric, Pyspark, Information Technology, Azure Synapse Analytics, Software Version Control, Data Pipelines - **Published:** November 13, 2026 - **Apply:** https://www.jobmonkeyjobs.com/career/28183216/Data-Engineer-Any-All-Cities-1210 ## About the Role * 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. 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., * Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 3+ years 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 5+ years experience in business analytics, data science, software development, data modeling, or data engineering + OR equivalent experience. * 1+ year(s) experience with data governance, data compliance and/or data security. * 3+ years of experience in data or analytics engineering, building and supporting production data solutions. * Experience developing Apache Spark/PySpark notebooks for large-scale data ingestion, transformation, enrichment, and processing. * Experience with Azure Synapse Analytics or similar distributed data platforms, including secure ETL and data-processing workloads.Proficiency in SQL and dimensional data modeling, transforming complex source data into curated datasets optimized for analytics and Power BI. * Experience building reliable production data pipelines, including validation, error handling, performance optimization, monitoring, and troubleshooting. * Ability to translate business and reporting requirements into scalable data solutions and collaborate across data engineering, BI, analytics, and architecture teams. * Hands-on experience with Microsoft Fabric Data Engineering, including Lakehouses, notebooks, Delta tables, OneLake, and SQL analytics endpoints. * Experience designing medallion/lakehouse architectures across raw, curated, and consumption-ready data layers.Understanding of enterprise data security and governance, including identity-based access, environment separation, and handling of sensitive data. * Experience with software engineering and DevOps practices for data solutions, including Git, code reviews, testing, CI/CD, and deployment across development, UAT, and production. * Experience using AI-assisted development tools and reusable AI skills to improve data engineering productivity, code quality, testing, and troubleshooting. Data Engineering IC3 - The typical base pay range for this role across the U.S. is USD $102,100 - $202,200 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 $133,800 - $219,200 per year. ## Description * Design, develop, test, and maintain secure, scalable data pipelines and processing solutions supporting UDP and enterprise analytics workloads. * Develop data engineering solutions using PySpark, SQL, Spark notebooks, and distributed data processing technologies across Microsoft Fabric and Azure Synapse environments. * Build and maintain curated data models and reusable datasets, applying sound dimensional modeling, data transformation, and data architecture practices. * Implement automated data quality, validation, monitoring, and production support capabilities to ensure reliable and trustworthy data products. * Optimize data pipelines and processing workloads for performance, scalability, reliability, and efficient resource utilization. * Apply engineering best practices including source control, code reviews, testing, CI/CD, documentation, and maintainable development patterns. * Implement appropriate security, privacy, access controls, and data governance requirements when processing sensitive enterprise and customer data. * Troubleshoot data pipeline, transformation, and production issues, performing root-cause analysis and implementing durable fixes. * Partner with analysts, engineering teams, and business stakeholders to translate reporting and analytics requirements into well-designed data solutions, communicate progress and technical tradeoffs, and resolve data issues. * Contribute to the continuous improvement of the UDP engineering platform by identifying opportunities to simplify pipelines, improve data architecture, increase reuse, and reduce operational complexity.