Kpler28d ago

Data Engineer - Dry Bulk Commodities

Budapest · BudapestFull-timemid

Data EngineeringData EngineerData & AI

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Quick Summary

Overview

At Kpler, we are dedicated to helping our clients navigate complex markets with ease. By simplifying global trade information and providing valuable insights,

Technical Tools

Data EngineeringData EngineerData & AI

At Kpler, we are dedicated to helping our clients navigate complex markets with ease. By simplifying global trade information and providing valuable insights, we empower organisations to make informed decisions in commodities, energy, and maritime sectors.

Since our founding in 2014, we have focused on delivering top-tier intelligence through user-friendly platforms. Our team of over 700 experts from 35+ countries works tirelessly to transform intricate data into actionable strategies, ensuring our clients stay ahead in a dynamic market landscape. Join us to leverage cutting-edge innovation for impactful results and experience unparalleled support on your journey to success.

Your future position

Kpler provides the most comprehensive tracking of commodity flows in dry bulk, and we are rapidly expanding our coverage. As a Data Engineer within our Dry Bulk market data operations team, you'll build the tools and analytical capabilities that enable our analysts to maintain the integrity of our cargo tracking data at scale. This role will bridge the gap between market analytics and engineering, developing tools and systems that make surgical data interventions more efficient and scalable.

Create scripts, CLI utilities, and web-based UIs (using tools like Streamlit) that empower analysts to perform data quality interventions more efficiently and at greater scale

Query and analyse substantial datasets to surface insights, identify data quality issues, and inform both tactical interventions and strategic improvements

Work alongside software engineers to develop backend tools and data pipelines that improve the overall quality and reliability of our tracking systems

Understand the day-to-day challenges analysts face and translate those pain points into technical solutions that reduce manual effort

Create clear documentation for tools, data processes, and analytical approaches to ensure knowledge sharing across the team

Develop automated checks and monitoring systems that proactively flag data anomalies before they require manual intervention

Rapidly test new approaches to data quality challenges, learning from what works and what doesn't, and scaling successful solutions

Experience extracting, transforming and analysing data from databases and APIs

Comfortability working with large, unstructured datasets too complex for standard spreadsheet tools

Experience tackling problems that have required learning new tools or technical approaches

Background working as part of a multidisciplinary team, particularly with mixed technical/non-technical colleagues

Proficiency in Python (or demonstrable ability to become proficient quickly)

Experience building CLI tools, or scripts that automate processes and improve team efficiency

Familiarity with Git-based workflows and code reviews

A mindset that prioritises delivering practical, workable solutions over chasing perfect ones that never ship