5+ years of professional data engineering experience with a track record of building and owning production data platforms.
Bachelor's degree in Computer Science, Mathematics, Information Technology, or a related field.
Cosmos DB (required) — partition strategy, indexing, change feed, TTL, multi-tenant patterns, RU optimization
Azure Data Explorer (ADX) (required) — cluster management, ingestion pipelines, KQL, update policies, retention, caching tiers
Azure SQL (required) — schema design, stored procedures, query optimization, ETL integration
Azure Event Hubs and Service Bus — high-throughput ingestion, consumer group management, dead-letter handling
Azure Functions — event-driven pipeline orchestration
Strong proficiency in KQL for ADX and SQL for Azure SQL; working proficiency in Python, pySpark and/or C#/.NET/JAVA/etc. for pipeline development.
Solid understanding of multi-tenant SaaS data architecture — isolation patterns, shared infrastructure cost management, tenant-aware querying.
Experience designing data models for time-series, telemetry, and event-driven workloads at scale.
Strong understanding of distributed data systems — partitioning, consistency tradeoffs, throughput vs. latency optimization.
Hands-on experience supporting and owning production SaaS platforms end-to-end.
Knowledge and expertise in using Git for distributed version control, ability to resolve merge conflicts, knowledge of Git branching strategies.
Knowledge and expertise in Azure DevOps deployment pipelines
Experience in utilizing AI in a data platform ecosystem.