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Kpler is a global intelligence platform founded in 2014 that simplifies complex trade information and provides actionable insights for the commodities, energy, and maritime sectors. The company employs over 850 experts across 69 countries and transforms intricate data into strategic intelligence for informed decision-making.
In this Backend Engineer role focused on commodities, you will build and maintain scalable data ingestion and processing pipelines for sensor, rail, and waterway data using Python, Kafka, and Airflow. You'll develop backend services using FastAPI and contribute to customer-facing web applications built with TypeScript and Vue. A key responsibility is implementing data validation, system monitoring, and alerting to ensure accuracy and completeness of noisy time-series data from field hardware.
You will operate owned services end-to-end, taking charge of deployments, observability, and participation in the team's on-call rotation. Close collaboration with data science and sensor operations teams is essential to transform raw telemetry into high-value, usable datasets for early customer adoption. You'll drive continuous platform health by participating in technical design discussions, improving CI/CD workflows, and delivering tested, well-documented code.
Kpler values decisive action, collaborative problem-solving, and a supportive approach to colleagues and clients. The company is committed to building a fair, inclusive, and diverse work environment.
REQUIREMENTS (Must-haves):
- 2 to 4 years of professional software engineering experience building and supporting production software
- Strong proficiency with Python and relational databases (SQL / Postgres)
- Proven experience deploying, debugging, and operating backend services in cloud environments (AWS preferred)
- Hands-on exposure to message queues, streaming platforms (e.g., Kafka), and RESTful API development
- Strong problem-solving skills with high agency to move from ambiguous requirements to working code
Nice-to-haves:
- Experience with FastAPI, Airflow, TypeScript, Vue, or Elasticsearch
- Prior exposure to time-series, IoT, remote sensor, or geospatial datasets
- Domain interest in energy, commodities, or logistics supply chains
