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Mid LevelFull-timePythonGit
Salary Range
$71,400 - $96,600 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are looking for a Data Engineer to join our Dry Bulk market data operations team at Kpler. This role involves building tools and analytical capabilities to maintain the integrity of our cargo tracking data at scale, bridging the gap between market analytics and engineering.
Key Responsibilities
- →Create scripts, CLI utilities, and web-based UIs (using tools like Streamlit) to empower analysts to perform data quality interventions more efficiently and at greater scale
- →Query and analyze 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
Requirements
- ✓Experience extracting, transforming, and analyzing 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 prioritizes delivering practical, workable solutions over chasing perfect ones that never ship
- ✓The inclination to take ownership of problems, using initiative to tackle issues, without waiting to be asked
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