Experience working on the internals of Cosmos DB or a comparable database, such as MongoDB, Amazon DynamoDB, or Apache Cassandra, and an understanding of the hardware and network limits that make them throttle, are especially valuable. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python These requirements include but are not limited to the following specialized security screenings: Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. 4+ years of experience building or operating large-scale distributed databases, such as Azure Cosmos DB, MongoDB, Amazon DynamoDB, or Apache Cassandra, including partitioning, indexing, and query performance. Experience finding and fixing throttling, hot partitions, or resource-heavy cross-partition queries in production databases. Experience building or improving the internals of Azure Cosmos DB, or of a comparable distributed database such as MongoDB, Amazon DynamoDB, or Apache Cassandra, in areas such as partitioning, replication, storage engines, query execution, indexing, or rate limiting. Deep, hands-on Azure Cosmos DB expertise: what drives the RUs each operation uses, physical partitions and splits, hierarchical partition keys, indexing policies, point reads and ReadMany versus cross-partition queries, change feed, consistency levels, multi-region accounts, autoscale, SDK (software development kit) retries, and diagnosing errors. Track record of making throttling go away quickly, raising availability, and cutting tail latency in production, with targeted, high-ROI changes and before-and-after measurements to prove it. Deep understanding of why distributed databases throttle, from per-partition throughput and storage limits to hardware, network, and replication overhead, and how to design data models that stay within those limits. Experience moving large or rarely queried content from a document database to Azure Blob Storage or comparable object storage, and serving it with point reads. Experience with zero-downtime data migrations and re-partitioning on live production systems. Experience designing rate limits, quotas, and noisy-neighbor isolation for a multi-tenant platform, and getting partner teams to cache on their side. Proficiency in C# and .NET, and experience with Azure services such as Azure Functions, Service Bus, Azure Cache for Redis, and API (application programming interface) Management. Understanding of modern AI technologies, including generative AI and foundation models, and experience writing AI skills and using agentic tools to engineer faster. Cut request unit (RU) consumption to make throttling go away, fast. Start with the queries that use the most RUs, where we see room for 50-90% reductions: turn cross-partition and fan-out queries into point reads from Cosmos DB or Azure Blob Storage, batched reads, or single-partition queries, and tune indexes. Pick work by return on investment. Ship several targeted fixes in parallel, each within days, and measure each one in production. Take on a larger redesign only when the data shows smaller fixes cannot get there, and deliver it in steps that each pay off. Fix hot partitions and size limits at the source: partition keys, document design, indexing policies, and API contracts that give high-traffic calls the keys they need for point reads. When data has to move, migrate it with zero downtime across all environments, including sovereign clouds. Decide what belongs in Cosmos DB. Move large, binary, unbounded, or rarely queried content to Azure Blob Storage or other storage, weighing latency, availability, and data-residency needs. Remove redundant and sequential database calls on our busiest paths with batching, request coalescing, and caching, and use retry budgets, load shedding, and graceful degradation so database pressure does not turn into failed requests. Work with product management and the teams that call us to give each caller a throttling budget. Find callers that request the same unchanged data again and again, help them cache on their side, and lower the budgets of the heaviest callers over time. Make success measurable: own the telemetry, dashboards, and alerts, RU use per API and per caller, partition skew, availability, and latency. Set targets, show the effect of every change, and spot limits before we hit them. Raise the bar through AI: write AI skills that review designs and code for Cosmos DB mistakes, find high-RU queries, and show engineers how to fix them, so your expertise scales across the team. Use agentic tools to build and validate fixes faster, following Responsible AI practices. Mentor engineers and represent us with the Azure Cosmos DB product group. Serve as a Designated Responsible Individual (DRI) in our 24/7 on-call rotation, lead data-tier incident response, and turn what we learn into lasting fixes.