Backend Engineer

About the role

We are looking for a Senior Backend Engineer to join Proxet and contribute to a long-term engagement with a UK-based technology company developing simulation and decision-support solutions for governments and enterprises.

 

Who are we looking for?

Skills & Experience:

• 7+ years of professional software engineering experience, with strong hands-on expertise in Python backend development.
• Experience building and maintaining production services, APIs, and integrations.
• Good understanding of distributed systems, microservice architectures, and asynchronous data processing.
• Experience with relational databases, preferably PostgreSQL, including schema design, query optimisation, and troubleshooting.
• Experience with data ingestion, processing pipelines, and integration of external data sources.
• Practical experience deploying and troubleshooting applications using Docker and Kubernetes.
• Experience with CI/CD pipelines, Git workflows, and automated testing.
• Strong debugging skills and the ability to investigate issues across services, data flows, and deployment environments.
• Experience using AI coding assistants in development, with the ability to review, validate, and take responsibility for generated code.
• Experience communicating directly with customers or external stakeholders to clarify requirements and resolve technical questions.
• Ability to explain technical concepts clearly to non-technical audiences and translate operational needs into engineering tasks.
• Strong ownership, sound technical judgement, and comfort working with evolving requirements.
• Understanding of the Ukrainian context and confidence in communicating with local organisations.
• Professional proficiency in Ukrainian and English for customer meetings, technical discussions, and written communication.
• Willingness and ability to travel to customer sites within Ukraine.
   

Nice to have:

• Hands-on experience with Kafka publishers and consumers.
• Experience with Dagster or similar data orchestration tools.
• Experience working with public-sector organisations, local authorities, or infrastructure operators.
• Experience adapting and deploying software in customer-managed or private-cloud environments.
• Familiarity with GCP, AWS, or Azure.
• Experience with Helm, OpenShift, and shell scripting.
• Familiarity with Parquet, object storage, and metadata management.
• Experience integrating AI agents, MCP interfaces, or LLM APIs.
• Familiarity with simulation platforms, digital twins, or decision-support systems.
• Knowledge of C++.
   

Responsibilities:

• Design, develop, and maintain Python backend services and APIs supporting customer applications and simulation workflows.
• Work directly with customers and external organisations in Ukraine to understand their needs, operational processes, and local constraints.
• Participate in customer meetings, clarify requirements, and explain technical options and trade-offs.
• Translate customer needs into implementation tasks, data requirements, and integration specifications.
• Adapt existing platform capabilities to local use cases and share customer insights with the wider engineering and product teams.
• Build and maintain data ingestion and processing pipelines using Dagster.
• Integrate customer data sources with platform services and containerised simulation models.
• Work with Kafka publishers and consumers to process simulation outputs.
• Design and optimise PostgreSQL schemas and queries, and support appropriate data storage solutions.
• Build reusable backend components and identify opportunities to apply customer-specific improvements across other deployments.
• Contribute to deployment and troubleshooting of containerised applications in Kubernetes-based environments.
• Maintain automated tests and CI/CD workflows to support reliable delivery.
• Collaborate with frontend engineers, platform specialists, modellers, and delivery stakeholders.
• Participate in code reviews and technical design discussions, documenting decisions and integration approaches.
• Use AI development tools effectively while maintaining code quality, correctness, and maintainability.
• Communicate progress, dependencies, and technical issues clearly to both internal teams and customer stakeholders.