Project description
You will join a long-term, enterprise-scale project for a global client in the investment banking domain. The project focuses on building and evolving a set of mission-critical backend services that support complex business workflows, regulatory processes, and system integrations across the bank's ecosystem.
The platform is based on a distributed microservices architecture, leveraging Spring Boot, event-driven communication, and BPM-driven process orchestration. The system operates in a cloud-enabled, containerized environment, with a strong emphasis on security, scalability, resiliency, and maintainability.
You will work closely with architects, QA engineers, DevOps, and business stakeholders in an agile setup, contributing not only code but also technical direction, best practices, and mentoring.
Responsibilities
- The Python Engineer's role is to design and implement scalable, production-grade backend systems and microservices. The person we are looking for should be able to develop clean, maintainable codebases, build and scale distributed services, implement well-known communication and integration patterns, and apply best practices for large-scale distributed systems.
SKILLS
Must have
- At least 8+ years practical experience in Python development and delivering production systems.
- Advanced Python proficiency, especially in scalable, clean code architecture and microservices (e.g., FastAPI, AsyncIO)
- Solid understanding of API integration patterns and inter-service communication (e.g., REST, Kafka)
- Experience with authentication and authorization mechanisms (e.g., OAuth2, JWT, Microsoft EntraId)
- At least two backend/data-intensive solutions successfully delivered to production, ideally involving high-throughput APIs, data pipelines, or search/retrieval systems
- Practical knowledge and hands-on experience with: Relational and NoSQL databases (e.g., PostgreSQL, MongoDB), caching layers (e.g., Redis), message queues, and containerization (Docker, Kubernetes)
- Strong grasp of software engineering fundamentals: data structures, algorithms, concurrency, asynchronous programming, and API design (REST/gRPC)
- Solid understanding of testing practices: unit, integration, and end-to-end testing (e.g., pytest), plus CI/CD pipelines
- Ability to evaluate trade-offs and select appropriate tools, frameworks, and architectural patterns for a given problem
- Experience with PoC development and iterating quickly based on results
- Familiarity with cloud platforms (e.g., Azure, AWS, GCP) and infrastructure-as-code
- Strong debugging, profiling, and optimization skills for high-performance Python applications
Nice to have
• Experience with observability and monitoring tooling (e.g., Prometheus, Grafana, OpenTelemetry)
• Experience with event-driven and streaming architectures