About Forecast

Forecast provides risk analytics and tools to insurers, agricultural businesses, and the public sector. By combining actuarial expertise, agricultural insight, and modern data technologies, Forecast helps clients understand and respond to risk with clarity and confidence.

Projects Overview

Forecast is seeking a Junior Data Engineer to support a growing portfolio of client-facing and internal data engineering projects across the insurance, agricultural, and public-sector verticals. The role focuses on building and maintaining reliable data pipelines, managing geospatial and environmental data workflows, and developing analytical datasets and decision-support tools across diverse client environments and data domains.

You will contribute to delivering practical and scalable data solutions, ranging from pipeline development and data modelling to dashboards and client-facing analytical tools, as part of Forecast’s expanding project portfolio.

What You Will Be Doing

• Build and maintain automated data pipelines, ETL/ELT workflows, and analytical environments across multiple client and internal projects.
• Ingest, process, and manage structured, geospatial, weather, satellite, and other external datasets relevant to agricultural and insurance risk domains.
• Contribute to data modelling and design clean, reusable analytical datasets that support reporting, risk modelling, and decision-support tools.
• Support the development of data-driven platforms and tools for internal and client-facing applications.
• Build and maintain dashboards, visualisations, and reporting solutions to communicate insights to internal teams and clients.
• Ensure data quality, integrity, and reliability across datasets and workflows.

Your Background

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related quantitative field.
• 1–3 years of experience in data engineering, analytics engineering, GIS, or a related technical role.
• Strong proficiency in SQL and Python or R.
• Hands-on experience designing, building, and maintaining ETL/ELT pipelines and automated data workflows.
• Solid understanding of data modelling principles with the ability to design clean, reusable analytical datasets.
• Strong analytical and problem-solving skills, including the ability to identify issues, optimise performance, and improve workflow reliability.
• A proactive, delivery-focused mindset with the ability to context-switch across varied project environments.