15h Left: Backend Engineer

2 weeks ago

Bengaluru, Karnataka, India RemoteStar Full time
Location: Remote — India (Bangalore Preferred)

Employment: Full-time

Level: 1–5 years of experience

About the Client:

They convert unstructured text (such as news, social media, and transcripts) into real-time, explainable sentiment signals. Their platform provides streaming insights, backtests, and APIs to support investment research, risk assessment, and product teams.

The Role: Node.js Backend Engineer

We're looking for a Node.js developer to design, build, and maintain scalable backend services powering our sentiment analytics stack. You'll work closely with data scientists and product teams to deliver low-latency, reliable APIs and real-time data pipelines.

What You'll Do:

- Build and maintain APIs: Develop REST/GraphQL/gRPC services with Node.js.
- Work on data pipelines: Integrate sources (transcripts, news, X/Twitter, Reddit), normalise content, and deliver real-time signals.
- Database design & performance: Model schemas in PostgreSQL/TimescaleDB, optimise queries, indexing, and caching for sub-second performance.
- Scalability & reliability: Implement message queues (Kafka/Redis), caching, monitoring, and error handling for high-throughput systems.
- Production systems: Write clean, tested, and secure code; contribute to CI/CD pipelines, logging, and observability practices.
- Collaboration: Partner with product, data science, and design teams to ship impactful features.

Tech Stack (You'll work with):

- Backend: Node.js, TypeScript, FastAPI (Python exposure a plus)
- Data & Messaging: Redis, PostgreSQL/TimescaleDB, Kafka (or Kinesis/PubSub)
- Infra: Docker, Kubernetes, Terraform, AWS/GCP
- Monitoring: Prometheus, Grafana, OpenTelemetry

Qualifications:

- 1–5 years of professional experience as a backend engineer (startup or high-growth preferred).
- Strong proficiency in Node.js.
- Solid understanding of REST/GraphQL APIs, relational databases, and caching layers.
- Hands-on experience with message queues (Kafka/Redis) and distributed systems.
- Familiarity with containerisation (Docker, Kubernetes) and cloud platforms (AWS/GCP).
- Strong coding practices: testing, CI/CD, logging, and monitoring.

Nice to Have:

- Exposure to NLP/LLM or ML-serving systems.
- Experience with time-series data, streaming pipelines, or real-time UIs.
- Knowledge of authentication/authorisation, multi-tenant systems, and rate limiting.
- Financial/alt-data integrations (e.g., X/Twitter, Reddit, earnings transcripts).

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