Backend Developer

About the Role

The ideal candidate should have a strong foundation in Python, FastAPI, and ORMs (like SQLAlchemy), with the ability to handle large datasets, design efficient APIs, and integrate real-time streaming and asynchronous task processing using tools such as Redis, Celery, and message brokers (Kafka/RabbitMQ).

You will collaborate closely with the data engineering and frontend teams to design, build, and optimize APIs and services for real-time data visualization, analytics, and monitoring.

Key Responsibilities

• Design, develop, and maintain scalable backend services using FastAPI and Python.
• Handle and process large volumes of structured and unstructured data efficiently.
• Build real-time data pipelines and streaming APIs for live dashboards and analytics.
• Integrate message brokers such as Kafka, RabbitMQ, or Redis Streams for event-driven architectures.
• Develop asynchronous background tasks and schedulers using Celery.
• Design and optimize database schemas using SQLAlchemy with PostgreSQL or MySQL.
• Implement caching, data persistence, and pub/sub mechanisms using Redis.
• Collaborate with frontend teams to expose clean, well-documented APIs for visualization dashboards.
• Ensure high performance, scalability, and fault tolerance of backend systems.
• Implement monitoring, logging, and performance profiling in production environments.

  Required Skills

• Strong proficiency in Python and experience with FastAPI (or similar frameworks like Flask, Django REST).
• Proficiency with ORMs especially SQLAlchemy and strong understanding of relational databases.
• Experience with Redis for caching, queuing, or pub/sub systems.
  Hands-on experience with Celery for distributed or asynchronous task execution.
• Familiarity with message brokers like Kafka, RabbitMQ, or Redis Streams.
• Experience building real-time or live data streaming APIs using WebSockets or erver-Sent Events (SSE).
• Strong grasp of data modeling, semantic layers (e.g., DBT, Dremio), and RESTful API design.
• Experience handling and visualizing large datasets efficiently.
• Understanding of containerization (Docker) and modern deployment practices (CI/CD, microservices).

  Nice to Have

  Experience with ClickHouse, TimescaleDB, or Elasticsearch for time-series or analytical workloads. Familiarity with frontend visualization tools such as Grafana, Apache Superset, or Plotly Dash. Experience integrating monitoring or metrics systems (e.g., Prometheus, Loki, OpenTelemetry). Exposure to CI/CD pipelines and microservice architectures. Knowledge of semantic modeling frameworks (like Cube.js, AtScale). Basic familiarity with data science workflows or ML model integration in production APIs.