Assess current ingestion pipelines, database design, and overall platform architecture
Identify root causes of performance bottlenecks and scaling limitations
Recommend and implement architectural improvements (data modeling, ingestion patterns, queueing, compute/storage separation, etc.)
Optimize MongoDB usage for large-scale ingestion and query workloads
Review and improve Kubernetes, Helm, and worker orchestration patterns
Work closely with the engineering team to implement fixes and best practices

🎯 Requirements

5+ years of experience
Strong background in distributed systems and data platform architecture
Deep experience with MongoDB at a very large scale
Hands-on expertise with Kubernetes, Helm, and containerized workloads
Experience designing and optimizing high-throughput ingestion pipelines
Familiarity with task queues and worker-based ingestion models (e.g., Celery or similar)

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