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Tech Stack
Tools & technologiesAWSCloudDockerEC2ETLLinuxPandasPostGISPostgresPythonSQL
About the role
Key responsibilities & impact- Clean, standardise and validate large field datasets including farmer registrations, surveys, polygons and photos
- Run data quality checks using the gate-based DQC framework and SOPs, and resolve flagged issues with analysts and field teams
- Reconcile collected data against master lists and reference data
- Identify and correct defects in data quality checks and recommend SOP improvements
- Build and maintain ETL pipelines from collection tools and PostgreSQL/PostGIS databases to analysis outputs and client deliverables
- Schedule and monitor automated jobs with logging, incremental loads and failure alerts
- Design and document data schemas, validation rules and data dictionaries for country projects
- Write clean, version-controlled and tested Python and SQL
- Process geospatial data and support compliance checks such as EUDR polygon validation
- Set up and maintain AI-assisted workflows using LLM tools for data cleaning, quality checks, report drafting and repetitive analyst tasks
- Build OCR and LLM-based document and image extraction pipelines with accuracy verification
- Maintain prompts, reusable skills, project context files and runbooks for consistent, auditable AI workflows
- Evaluate AI versus deterministic scripts and track cost, accuracy and data privacy risks
- Train analysts to use AI tools safely and effectively
- Identify project risks and issues, propose practical solutions, and prepare dashboards, reports and delivery files
- Develop and evaluate operational processes and procedures
- Draft technical documentation, SOPs and training materials, and train analysts and field staff
- Provide the manager with daily, weekly and ad-hoc updates
Requirements
What you’ll need- Bachelor’s degree in Computer Science, Statistics, Data Science, Information Systems, Economics, Geoinformatics, or a related field
- Minimum of 2 years of experience in data analysis or data engineering
- Demonstrated experience cleaning large, messy field or survey datasets end to end
- Portfolio, GitHub profile or work samples showing pipelines or automations built
- Strong Python, including pandas and scripting for automation
- Strong SQL, with experience in PostgreSQL
- Experience building and scheduling ETL pipelines
- Experience using Git and working on Linux or cloud servers such as AWS EC2
- Hands-on experience using LLMs and AI tools in real workflows, including prompt design and checking AI outputs for accuracy
- Familiarity with mobile data collection tools such as ODK, KoboToolbox, CommCare, or SurveyCTO
- Experience with PostgreSQL; PostGIS is a plus
- Experience with R is a plus
- Experience building agentic or multi-step AI workflows, self-hosted/open-source models, OCR and document extraction, geospatial tools, EUDR/deforestation compliance data, orchestration tools or Docker is nice to have
Benefits
Comp & perks- High-performance, multicultural team environment
- Opportunities to work on global agricultural insurance projects across Africa, Asia and Latin America
- Training analysts and field staff on new systems and AI tools
- Professional development through exposure to data engineering, geospatial data, AI workflows and automation