🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
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 Microsofts commercial business into a frontier organization. We bring together AInative 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 senior 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 Senior 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 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 Strong data modeling skills dimensional/semantic modeling, schema design, normalization/denormalization trade-offs, and building reusable curated models across domains. Own engineering fundamentals Enterprise-grade logging, telemetry, evaluations, regression, performance testing, security, and continuous learning across every layer of the platform. Advance data governance Build with data tagging, RBAC/attribute-grained policy, lineage, audit telemetry, compliance, and data-quality standards baked in. Tech Stack You'll Work With Data foundation & compute Azure Fabric OneLake, ADLS Gen2, Azure Synapse, Azure Databricks Azure SQL Hyperscale, Cosmos DB Fabric Graph, curated semantic models (NL2SQL / NL2DAX) Agentic & AI layer Agent orchestration Copilot Studio Flow, MCP endpoints / FastAPI web apps Azure AI Foundry LLMs, SLMs, fine-tuned models; graph-powered routing and grounding Qualifications Required 9+ years of software/data engineering experience building production data pipelines at scale. Strong programming skills (e.g., Python, and/or Scala) and expert SQL. Hands-on experience with large-scale distributed data processing (Spark/Databricks, Synapse) and cloud data lakes (ADLS Gen2 / Fabric OneLake). Experience designing curated/semantic data models and delivering KPIs to business stakeholders. Solid grounding in data governance, security, lineage, and data quality. Ability to translate ambiguous business questions into robust, well-modeled data products. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.