Python Engineer (Backend & Data Aggregation)

Role & responsibilities

We are looking for a Python Engineer (2-4 years) who's strong in backend development and has hands-on experience implementing aggregation and data computation use cases such as device-level rollups, metrics computation, time-based summaries, or multi-source joins.

Youll work closely with platform, data, and product teams to design efficient aggregation logic and APIs that serve real-time and historical analytics, and youll help make Condenses data platform more intelligent and scalable.

• Design and implement data aggregation logic for device, customer, or time-window-based metrics using Python.
• Build clean, maintainable backend services or microservices that perform aggregations and expose results through APIs or data sinks.
• Work with internal teams to translate business or analytics needs into efficient aggregation pipelines.
• Optimize data handling caching, indexing, and computation efficiency for large-scale telemetry data.
• Collaborate with DevOps and data teams to integrate with databases, message queues, or streaming systems.
• Write high-quality, tested, and observable code ensuring performance and reliability in production.
• Contribute to design discussions, reviews, and documentation across backend and data infrastructure components.

Preferred candidate profile

• 2-4 years of professional experience as a Python Developer / Backend Engineer.
• Strong proficiency with Python (async programming, data structures, I/O, concurrency).
• Experience with data aggregation, metrics computation, or analytics workflows (batch or incremental).
• Sound understanding of REST APIs, microservice architecture, and database design (SQL/NoSQL).
• Familiarity with cloud-native development and containerized deployment (Docker, Kubernetes).
• Hands-on with data access and transformation using libraries like pandas, SQLAlchemy, or FastAPI/Flask for backend services.
• Excellent debugging, profiling, and optimization skills.

Good to Have

• Exposure to real-time data pipelines (Kafka, Kinesis, Pulsar, etc.) or streaming frameworks (Kafka Streams, ksqlDB, Faust).
• Experience with time-series databases or analytics stores (ClickHouse, Timescale, Druid, etc.).
• Understanding of event-driven or stateful aggregation patterns (tumbling/sliding windows, deduplication).
• Familiarity with CI/CD, observability tools (Prometheus, Grafana), and monitoring best practices.
• Experience working in IoT, mobility, or telemetry-heavy product environments.