Principal Software Engineer
About The Position
The Commerce organization builds and operates scalable, resilient, AI-powered data platforms that transform complex financial and infrastructure data into trusted insights. The Principal Software Engineer will architect, design, and develop a large-scale data platform that provides actionable insights into data center infrastructure capacity utilization and product consumption revenue, enabling business teams to make data-driven decisions. The role combines cloud-native distributed systems, advanced data engineering, and AI-powered software engineering. The engineer will define technical vision and architecture for platforms that turn infrastructure telemetry, consumption, and revenue data into explainable insights supporting capacity planning, infrastructure optimization, operational efficiency, and CapEx investment decisions. The position owns architecture for data ingestion, processing, and analytics modeling, and uses AI-assisted development and agentic engineering workflows to accelerate software delivery, modernize data systems, and improve developer productivity, system reliability, and analytical accuracy. This is a technical leadership role requiring hands-on engineering expertise, architectural judgment, comfort navigating ambiguity, and the ability to influence cross-organizational initiatives while delivering measurable business impact. The position requires working onsite three days a week in Microsoft’s Redmond, Washington offices.
Requirements
- Bachelor’s degree in Computer Science or a related technical field and 6+ years of technical engineering experience coding in languages such as Scala, Python, C#, or Java, or equivalent experience.
- Pass the Microsoft Cloud background check upon hire or transfer and every two years thereafter.
Nice To Haves
- Expertise architecting, building, and operating production-grade, cloud-native data platforms using Microsoft Fabric, Azure Synapse, Azure AI Foundry, Azure Machine Learning, or equivalent technologies.
- Deep proficiency in Apache Spark, Delta Lake, distributed systems, SQL, or large-scale batch and streaming data processing.
- Programming skills in Scala, Python, C#, Java, or equivalent, focused on maintainable, testable, high-performance production software.
- Extensive experience in data modeling, analytical data systems, and SQL-based analysis, including translating ambiguous business requirements into actionable metrics and insights.
- Ability to optimize distributed workloads, compute, storage, query execution, data partitioning, and processing costs.
- Experience building reliable data platforms with data quality, observability, governance, lineage, and operational excellence.
- Understanding of statistical analysis, time-series analytics, anomaly detection, and forecasting.
- Practical experience applying AI coding assistants, LLMs, and agentic workflows to software development, code modernization, testing, and engineering automation.
- Understanding of RAG, embeddings, semantic search, tool calling, and agent orchestration, with experience implementing security, correctness, reliability, and human oversight controls.
- Ability to lead cross-team technical initiatives, influence architecture and engineering strategy, navigate ambiguity, and deliver measurable business outcomes.
Responsibilities
- Architect highly scalable distributed data systems that process large volumes of infrastructure telemetry and cloud product consumption data.
- Design reusable data processing frameworks, canonical data models, and data contracts that accommodate evolving business requirements.
- Establish data engineering patterns for incremental processing, late-arriving data, historical backfills, reconciliation, lineage, and schema evolution.
- Lead architectural decisions across cloud data platforms, distributed compute engines, data lakes, streaming and batch processing, and analytical serving systems, balancing freshness, correctness, performance, and cost.
- Deliver actionable, explainable insights using AI and advanced analytics to support business impact and data-driven decision-making.
- Design AI-powered software engineering workflows using coding agents, RAG, domain knowledge repositories, and tool-integrated agents to accelerate development and modernization of large-scale data systems.
- Apply AI to analyze legacy codebases, map dependencies, generate implementation plans, automate refactoring, and improve test generation and code quality.
- Develop agentic workflows to streamline data pipeline development, code reviews, failure triage, root-cause analysis, and operational knowledge discovery.
- Establish governance and quality guardrails for AI-generated code, including automated testing, data correctness validation, security reviews, and production-readiness checks.
- Drive measurable improvements in engineering productivity, development lead time, test automation, defect reduction, pipeline reliability, observability, and recovery time.
- Embody Microsoft’s culture and values.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Principal
