If you are excited by the challenge of designing distributed systems that process petabytes of data for the world's most advanced AI models, this is your team. We are not looking for someone to just write queries or maintain legacy pipelines. We are looking for Systems Builders—engineers who understand the internals of distributed compute, who treat data infrastructure as a product, and who want to architect the backbone of Microsoft Copilot.

Core Platform Engineering: Design and build the underlying frameworks (based on Spark/Databricks) that allow internal teams to process massive datasets efficiently, abstracting away the complexity of "ETL" into self-service infrastructure.

Distributed Systems Architecture: Modernize our data stack by moving from batch-heavy patterns to event-driven architectures, utilizing modern streaming architecture to reduce latency for AI inference.

Unstructured AI Data Pipelines: Architect high-throughput pipelines capable of processing complex, non-tabular data (documents, code repositories, chat logs) for LLM pre-training, fine-tuning and evaluations datasets.

AI Feedback Loops: Engineer the high-throughput telemetry systems that capture user interactions with Copilot, creating the critical data loops required for Reinforcement Learning and model evaluation.

Infrastructure as Code: Treat the data platform as software. Define and deploy all storage, compute, and networking resources using IaC (Bicep/Terraform) rather than manual configuration.

Data Reliability Engineering: Move beyond simple "validation checks" to build automated governance and observability systems that detect anomalies in the data mesh before they impact downstream models.

Compute Optimization: Deep-dive into query execution plans and cluster performance. Optimize shuffle operations, partition strategies, and resource allocation to ensure our platform is as cost-efficient as it is fast.