> Markdown version of [/jobs/ext/3751180-infrastructure-data-analytics](https://www.wearedevelopers.com/jobs/ext/3751180-infrastructure-data-analytics). 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). --- # Infrastructure Data & Analytics - **Company:** Microsoft - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $119,800.0 - $234,700.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Business Analytics Applications, Data Analysis, Big Data, Computer Engineering, Data Architecture, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Distributed Computing Environment, Fault Tolerance, Microsoft Office, Software Engineering, Supercomputing, Data Ingestion, Build Management, Kubernetes, Information Technology, Data Analytics, Data Pipelines - **Published:** October 15, 2026 - **Apply:** https://www.adzuna.com/details/5926266809 ## About the Role * Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 4+ years of technical experience in data engineering, data science, analytics, software development, or data modeling, with increasing technical ownership + OR equivalent experience * 4+ years experience with distributed data processing frameworks and large-scale data systems, * Master's Degree in Computer Science or related technical field AND 10+ years of technical experience in data engineering, data science, analytics, software development, or data modeling, with increasing technical ownership + OR equivalent experience * Proven technical leadership in data engineering, analytics platforms, or large-scale telemetry systems * Hands-on experience with ETL orchestration frameworks such as Airflow, Dagster, or similar * Experience communicating complex technical systems and concepts to senior leaders Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $160,200 - $261,000 per year. ## Description Our mission is to build AI that amplifies human potential and empowers people around the world. We strive to deliver breakthroughs that advance science, education, productivity, and global well-being. We're also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact. If you're a brilliant, highly-ambitious and low ego individual, you'll fit right in-come and join us as we work on our next generation of models! MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location. This expectation is subject to local law and may vary by jurisdiction. Responsibilities * Act as the technical lead and owner for infrastructure analytics across compute, storage, and networking. * Design and build durable, scalable data pipelines that ingest telemetry from clusters, schedulers, health systems, and capacity trackers into Data Warehouse * Define and standardize core metrics and semantics (e.g., utilization, occupancy, MFU, goodput, capacity readiness, delivery-to-production). * Architect and maintain self-service dashboards and APIs for fleet, cluster, and squad-level visibility. * Partner closely with stakeholders across Supercomputing Infra, Researchers, Strategy and Executives to ensure metrics reflect operational and business reality. * Implement robust and fault-tolerant systems for data ingestion and processing. * Lead data architecture and engineering decisions, applying strong technical judgment to proactively shape executive-level discussions and decisions. * Identify data gaps and instrumentation issues; drive fixes by influencing upstream engineering teams. * Establish data quality, validation, documentation, and governance so metrics are trusted and repeatable. * Embody our culture and values.