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Selectika AI helps retailers turn messy product data into clean, structured, channel-ready catalogues. We ingest large catalogues, enrich items, and sync to e-commerce and marketing channels like Google Merchant Center, Meta, and TikTok.
What you will do● Own services that power the catalogue pipeline: ingest, transform, enrich, validate, sync, and exports
● Design and ship TypeScript Node.js microservices in a monorepo with shared packages
● Build and evolve GraphQL and REST APIs for internal teams and external partners
● Migrate the frontend from Angular to Vue 3 and set solid foundations with Vite and reusable components
● Optimize large data flows using streaming parsers for CSV XML JSON APIs with delta updates and idempotent writes
● Implement storage layers using Prisma and PostgreSQL Aurora RDS plus MongoDB for document workloads and Redis for caching and queue metadata
● Work with RabbitMQ workers for feed parsing, enrichment steps, and background jobs with backpressure and retries
● Operate on AWS EKS using Helm and Karpenter with KEDA for queue-aware autoscaling CloudWatch for logs and metrics and S3 for assets
● Measure and improve performance reliability and cost using tracing structured logging and SLOs
● Collaborate closely with product and data teams to turn retailer feeds into validated, channel-ready data
Why this is interesting day to day
● You ship fast. Infra is Kubernetes on AWS with Helm charts per service, GitHub Actions pipelines, and Karpenter autoscaling so deploys are quick and safe
● You work on real throughput. Large catalogues, high message rates, and memory-sensitive processing
● You see impact. Changes land across dev and main namespaces with dashboards and alerts, and you can prove wins with metrics
Our stack
● Language TypeScript Node.js monorepo. Some Python where useful
● Data PostgreSQL Aurora RDS with Prisma. MongoDB. Redis
● Messaging RabbitMQ
● APIs GraphQL and REST
● Infra AWS EKS with Karpenter and Helm. KEDA optional. CloudWatch. S3
● Tooling GitHub Actions Docker pnpm TurboRepo
● Product features Tagging enrichment similarity and feed exports
Required experience
● 4 plus years building production services with TypeScript and Node.js
● Strong SQL and data modeling with PostgreSQL and hands-on Prisma
● Experience with large data feeds, streaming parsers, and memory-efficient ETL
● Operating services on Kubernetes and AWS with CI CD and observability
● Production experience with message queues preferably RabbitMQ including dead-lettering and retries
● Performance tuning for high-throughput APIs and batch workers
● Ownership mindset and clear communication
Nice to have
● Vue 3 and Vite. Experience migrating from Angular
● GraphQL schema design and gateway patterns
● Image pipelines on S3 with presigned URLs thumbnails and CDNs
● E-commerce feeds Google Merchant Center Meta TikTok Shopify WooCommerce
● KEDA based autoscaling from RabbitMQ metrics
● pgvector or similar similarity search
What success looks like
● You ship a service or worker into the pipeline with dashboards and alerts
● You move a feeder to streaming with backpressure and reduce p99 latency on a core API
● You lead an improvement across dev and main including queue policies and autoscaling that lowers cost and increases throughput
Interview process
● Intro call 20 minutes fit and role context
● Technical deep dive 60 minutes architecture and code review on a real service from our domain
● Practical pair session 90 minutes build a streaming feed parser and persist via Prisma plus a small API no take-home
● Final culture and product session 30 minutes with founders and PM
How to apply
Send GitHub or relevant repos plus a short note about a high-throughput system you built to [email protected]