Overview
Microsoft is a company where passionate innovators come to collaborate, envision what can be, and take their careers to places they cannot achieve anywhere else. This is a world of more possibilities, more innovation, more openness, and more impact.
Commercial Engineering & AI CEAI partners closely with stakeholders to accelerate the transformation of Microsoft’s commercial business into a frontier organization. We bring together AI‑native engineering, modern platforms, and deep commercial insight to reimagine how work gets done - at scale and with impact. Our mission is to unlock new ways of operating through intelligent systems while creating the conditions for our teams to do the most meaningful work of their careers.
Customer Success Engineering CSE within the Commercial Engineering and AI CEAI organization builds and manages critical products and services that Microsoft runs on. We pursue big ideas that power transformational advances for Microsoft and its customers while helping teams work smarter, faster, and more securely every day.
We are looking for Sr. software engineer to work on petabyte-scale commercial data platform that turns raw operational and business signals into grounded intelligence — powering agents that help leadership and every persona make faster, better decisions. As a Software Engineer – Data, you will design and build the pipelines, curated semantic models, and agents that convert raw data from 200+ transactional sources into trusted KPIs, recommendations, and deep-linked actions across the business. You will work across the full stack of a modern data estate — from ingestion and large-scale compute, to curated logical models, to the agent orchestration layer that answers natural-language questions with facts, reasoning, and recommendations.
Responsibilities
Responsibilities:
- Design & Build petabyte-scale data pipelines — Design, develop, and operate reliable ingestion and transformation pipelines.
- Proven ability to design system architecture for large-scale data platforms — data modeling, layering, scalability, reliability, and cost trade-offs.
- Convert raw data into intelligence — Transform staging → canonical → curated data products, building purpose-built, curated semantic/logical models.
- Mine logs to drive optimization — Instrument, collect, and analyze telemetry and operational logs to surface anomalies, root-cause trends, and process-optimization opportunities.
- Design & build agents for leadership and other personas — Develop AI-native agents that decompose natural-language questions, route them to the right domain skill, and return insights driven by reasoning, insights, and recommendations.
- Deliver KPIs that measure business outcomes — Partner with stakeholders to define, model, and ship the KPIs and persona dashboards that quantify business impact and inform executive decision-making.
- Rapidly prototype and prioritize ideas
- Good data modeling skil
