Python Engineer ? Backend & Data Aggregations

Job Description

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