We are looking for a skilled Python Developer with 3+ years of work experience to design, develop, and maintain scalable applications and backend services. The ideal candidate should have strong Python programming skills, good problem-solving ability, and experience working with APIs, databases, and modern development frameworks.

Key Responsibilities:

• Design, build, and operate distributed batch and streaming pipelines that process highvolume data.
• Build high-performance, production-grade REST (and where appropriate, gRPC) APIs that serve data to web, mobile, and AI applications.
• Work with Kafka or rabbitmq like similar message queues, Spark/PySpark, and Flink/Storm to build ingestion, enrichment, and aggregation workflows with strong guarantees around ordering, idempotency, and fault tolerance.
• Model and optimise data in RDBMS database and NoSQL database and object storage
  (MinIO/S3), including partitioning, indexing, and spatial query tuning.
• Profile and optimise Python code for throughput and memory: async I/O, multiprocessing, vectorisation, and knowing when to drop to a faster tool.

Preferred Skills :

• Professional software development, with Python as your primary language.
• Deep Python expertise: concurrency (asyncio, threading, multiprocessing, the GIL), typing, packaging, testing, and performance profiling.
• Proven experience building and running distributed data pipelines in production, with at least one of Spark, Flink, Kafka Streams, Beam, Storm or similar.
• Hands-on experience with Kafka or another message broker, including consumer groups, partitioning, delivery semantics, and back-pressure.
• Strong API design skills using FastAPI, Django/DRF, or Flask: versioning, pagination, auth
  (OAuth2/JWT), rate limiting, and OpenAPI documentation.
• Solid SQL and PostgreSQL knowledge, including query plans, indexing, and schema design.
• A strong grasp of distributed systems fundamentals: consistency, partitioning, retries,
  exactly-once vs at-least-once, and failure modes
• Time-series and IoT data handling (TimescaleDB, InfluxDB, or high-frequency telemetry).
• Workflow orchestration with Airflow, Prefect, or Dagster.
• Exposure to ML pipelines or serving models behind APIs