Hi,
Please find the below JD and send the suitable profiles asap.
Role -Typescript Architect
Location: Denver, CO (Hybrid)
IF CANDIDATE IS GOOD, THEY ARE OPEN TO REMOTE BUT WILL COME TO THE OFFICE 2 DAYS A MONTH
Job Description
TypeScript Architect with a strong background in event-driven microservices, real-time data pipelines (Kafka), and cloud-native application design. The ideal candidate will drive the technical architecture for scalable backend systems leveraging TypeScript/Node.js, DynamoDB, Kafka, and advanced caching and performance optimization strategies.
· Architect scalable backend systems using TypeScript/Node.js and event-driven patterns (Kafka, SNS/SQS, or equivalent).
· Design and implement streaming and asynchronous processing pipelines for large-scale workloads.
· Define and enforce data modeling and partitioning strategies for DynamoDB to handle high-write workloads efficiently.
· Establish and optimize caching layers (Redis, Elasticache, DAX, or in-memory caching) for high-performance APIs.
· Perform profiling and performance tuning across the stack (CPU, memory, network, database, I/O).
· Define API contracts, message schemas (Avro/JSON), and versioning standards for inter-service communication.
· Implement observability practices—metrics, tracing, and structured logging—to proactively monitor performance bottlenecks.
· Collaborate with DevOps to ensure CI/CD pipelines, IaC (CloudFormation/Terraform), and autoscaling policies support architectural goals.
· Mentor engineering teams on TypeScript best practices, async programming, and microservice resilience patterns(circuit breaker, retry, backoff, etc.).
· Partner with stakeholders to evolve system architecture in alignment with business growth and product roadmaps.
Required Skills
· Strong expertise in TypeScript & Node.js, including async/await, streams, and worker threads.
· Kafka architecture & tuning: partitioning, consumer groups, rebalancing, offset management, and schema evolution.
· AWS DynamoDB: data modeling, secondary indexes, TTL, streams, DAX, and best practices for large-scale design.
· Caching frameworks: Redis, Memcached, DAX, or CDN edge caching.
· Performance tuning & scalability: CPU profiling, async I/O optimization, connection pooling, and load testing.
· API design: REST and GraphQL, including schema federation and gateway design.
· Monitoring tools: Datadog, Prometheus, CloudWatch, or Splunk.
· DevOps awareness: CI/CD (GitHub Actions, Jenkins), container orchestration (EKS/Kubernetes), and IaC tools.
Srini
vasuatsrinavdotnet
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