What Youll Do Assist in building, maintaining, and optimising automated data pipelines and workflows. Ingest, process, and manage structured, geospatial, satellite, weather, and external datasets. Support the development of analytical datasets, dashboards, reporting solutions, and client-facing tools. Contribute to internal platforms and data-driven applications used for risk analysis and decision-making. Assist with ETL/ELT processes, data transformation, and integration workflows. Monitor and improve data quality, integrity, reliability, and performance across systems. Collaborate with cross-functional teams on data, analytics, and geospatial initiatives. Continuously learn and contribute ideas to improve workflows, automation, and engineering practices. Requirements Essential Bachelors degree in Computer Science, Engineering, Data Science, Information Systems, GIS, or a related quantitative field. 13 years experience in Data Engineering, Analytics Engineering, GIS, Data Analytics, or a similar technical role. Strong SQL and Python or R skills. Exposure to building and maintaining ETL/ELT pipelines and automated workflows. Understanding of data modelling principles and reusable analytical datasets. Strong analytical thinking and problem-solving abilities. Ability to troubleshoot issues, optimise workflows, and work with large datasets. A proactive attitude with a strong willingness to learn and grow. Good communication skills and ability to work within a collaborative team environment. Advantageous Exposure to GIS, geospatial, satellite, environmental, agricultural, or weather-related datasets. Experience with cloud platforms, APIs, or modern data tooling. Familiarity with dashboards and data visualisation tools. Understanding of risk modelling or analytics environments. What is on Offer Opportunity to join a fast-growing agritech and risk analytics company. Hands-on exposure to meaningful, real-world projects and modern technologies. A collaborative, close-knit team where your work has visible impact. Exposure to agricultural intelligence, risk modelling, and public-sector data ecosystems. Career growth and continuous learning opportunities. Hybrid working model with flexibility between office and remote work. Competitive salary aligned with experience and skills