Python Engineer — Backend & Data Aggregations

Contract duration :

Role Overview :

We’re 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.

You’ll work closely with platform, data, and product teams to design efficient aggregation logic and APIs that serve real-time and historical analytics, and you’ll help make Condense’s data platform more intelligent and scalable.

Key Responsibilities :

· 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.

Required Qualifications :

· 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.

What Success Looks Like :

· You deliver robust, scalable aggregation logic that enables downstream analytics and dashboards.

· Code is clean, performant, and maintainable, following engineering best practices.

· Aggregation jobs and APIs are well-monitored and observable, enabling smooth production operation.

· You work effectively across teams — platform, DevOps, and product — to deliver data-backed insights faster.

· You continuously learn and adopt best practices from the Python and data engineering ecosystem