Fullstack Engineer

This role is suitable for a Middle-level Full-Stack Engineer with at least 3 years of professional experience building and operating production web applications.

The candidate should have strong skills in TypeScript/JavaScript, Vue or React, Python with FastAPI or Node.js, REST/GraphQL APIs, microservices, databases, AWS, Docker, CI/CD, automated testing, and observability.

They should be familiar with owning features end to end, designing data models and APIs, developing responsive interfaces and backend services, deploying and monitoring cloud applications, and collaborating with product, design, and data teams.

How would you structure a full-stack feature using a TypeScript frontend and a Python FastAPI backend?

I would begin by defining the API contract and data model, then implement the FastAPI endpoint with validation, authorization, and tests. On the frontend, I would build typed API clients, reusable components, loading and error states, and integration tests before deploying the feature through CI/CD.

What are the key considerations when designing a high-performance REST API for real-time trade data?

I would use clear resource models, pagination or streaming where appropriate, efficient filtering, caching, and database indexes. I would also define consistent error responses, authentication, rate limits, observability, and backward-compatible versioning.

When would you choose GraphQL over REST for a maritime analytics application?

GraphQL is useful when clients need flexible projections across related datasets and want to avoid over-fetching or multiple requests. REST may be preferable for simple resource operations, strong HTTP caching, public integrations, or predictable operational behavior.

How would you design a microservice that processes real-time shipment or market events?

I would define an event schema, use an appropriate broker or streaming system, and make consumers idempotent so retries do not create duplicate effects. The service should include durable state management, dead-letter handling, metrics, tracing, and clear ownership of failures.

How do you approach database design for complex global trade data?

I would identify access patterns first, then choose relational or NoSQL storage based on consistency, query complexity, scale, and update characteristics. I would normalize transactional data where useful, add targeted indexes, and use denormalized or materialized views for high-volume analytical queries.

How would you build a responsive React or Vue interface for large datasets?

I would use server-side filtering, pagination or virtualization, debounced searches, and incremental loading rather than rendering the entire dataset. I would also manage client state carefully, provide clear loading and error states, and keep components modular and accessible.

What are the benefits and challenges of a micro frontend architecture?

Micro frontends allow teams to deploy and own independent parts of a large application, improving autonomy and reducing coordination bottlenecks. They also introduce challenges around shared dependencies, routing, consistent UX, performance, and cross-application communication.

How would you containerize and deploy a FastAPI and TypeScript application on AWS?

I would create small, multi-stage Docker images, run the services as non-root users, and configure environment-specific settings securely. CI/CD would build and scan images, run tests, publish artifacts, and deploy to an AWS runtime such as ECS or Kubernetes with health checks and rollback support.

What would you monitor with Datadog in a production full-stack system?

I would monitor latency, throughput, error rates, saturation, database performance, queue depth, and frontend performance such as page-load and API timings. Structured logs, distributed traces, dashboards, and actionable alerts would help correlate customer impact with specific services or deployments.

How do you use automated testing and AI-assisted development tools responsibly?

I would combine unit, integration, API, and end-to-end tests with code review and CI quality gates. AI tools can accelerate scaffolding, refactoring, and test generation, but all generated code must be reviewed for correctness, security, performance, licensing, and consistency with the system design.

Kpler is a global market-intelligence company helping organisations make smarter decisions across commodities, energy, maritime, and logistics. As a Fullstack Engineer, you’ll build responsive web applications and high-performance APIs, own features from design through production, and collaborate with product, design, and data teams. The stack includes TypeScript, Vue, React, Python, FastAPI, GraphQL, AWS, Docker, CI/CD, and Datadog, with AI-assisted development tools encouraged. This is a hybrid, full-time role in Greece; salary is not disclosed, and it’s worth applying for the chance to shape real-time trade intelligence at a diverse, global company.