Backend Engineer (41886)

I am looking for an experienced Backend Engineer with strong Python skills and relational database expertise to design and operate backend services and APIs. You will collaborate with data scientists and traders, optimize data models and pipelines for analytics and machine learning, deploy cloud-native services (Docker, Kubernetes, Cloud Run), and mentor junior engineers while shaping backend best practices.

🚀 Project
- design, develop, and operate backend services and APIs in Python (Django and related frameworks)
- build and maintain data pipelines and transformations powering analytics and machine learning workflows
- model and optimize relational data (PostgreSQL/TimescaleDB, BigQuery), ensuring robust schemas and efficient queries
- integrate services with message/streaming systems (Kafka, Pub/Sub) and external data sources
- ensure performance, reliability, security, and observability of backend systems in production
- work with data scientists, quants, and traders to productionize models and algorithms and expose them via APIs or internal tools
- contribute to cloud-native deployments (Docker, Kubernetes, Cloud Run) and CI/CD practices
- mentor junior engineers and help define backend engineering best practices

🎯 Skills
- 5+ years of experience as a backend or software engineer working on production systems
- strong proficiency in Python for backend development
- solid experience with relational databases (PostgreSQL): schema design, performance tuning, complex querying
- experience designing and building clean, well-structured APIs used by other services or teams
- ability to break down complex problems and deliver simple, robust solutions
- strong coding standards: write clean, maintainable, tested, and documented code
- effective communication and collaboration with cross‑functional teams (data scientists, analysts, traders, product)

💡 Nice to have
- experience with Google Cloud Platform or another major cloud provider
- familiarity with containerization and orchestration (Docker, Kubernetes) and modern CI/CD pipelines
- exposure to time‑series data or data-intensive systems (analytics, monitoring, trading)
- experience integrating machine learning or optimization pipelines into production backends

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