Senior Data Analyst - Company and Ownership Data

About the role

Kpler provides global trade intelligence for commodities, energy and maritime sectors. As a Senior Data Analyst in the Data Platform team, you will be the subject matter expert behind the company and ownership datasets, ensuring the accuracy, structure and ongoing development of company-level data, including ownership, management, operator and corporate hierarchy information. The role starts with maritime companies and will expand to other asset classes such as ports, terminals, refineries, commodities and infrastructure.

Key responsibilities

• Own the end-to-end quality of assigned vessel particulars datasets, including specifications, ownership, classification and technical attributes.
• Research and validate vessel data from multiple sources, ensuring accuracy, consistency and completeness.
• Analyse datasets to identify gaps and anomalies, run validation checks and improve data quality and coverage.
• Create and maintain SQL- and Python-based workflows to automate data collection, cleansing and enrichment.
• Develop dashboards and reports on vessel data quality for Market Analysts.
• Support integration of new vessel data sources, including scoping, testing and validation of incoming feeds.
• Investigate and resolve data quality issues raised internally or by customers, documenting data logic, business rules and definitions.
• Collaborate with Product, Engineering and other analysts to turn data quality findings into platform and pipeline improvements.

Required profile

• 2-4 years of experience in a data analyst, data quality or data operations role.
• Demonstrable knowledge of vessel data and maritime shipping concepts; experience with large, real-world vessel particulars datasets.
• Experience in the maritime industry.
• Strong analytical, ownership and communication skills, able to work independently and with technical and non-technical stakeholders.

Required skills

• SQL for querying, cleansing and analysis.
• Data validation, monitoring and quality-improvement techniques.
• Nice to have: data visualisation tools, ETL processes and web scraping, Python for analysis and automation, API usage, data pipelines, and familiarity with maritime data sources.