Data Analyst (Remote)

About this role

SafeStart Global Inc seeks a Data Analyst for a remote role based in Nigeria or South Africa, with activity in Kano State, Nigeria. The position supports the organisation’s monitoring and evaluation framework, handling data from field submissions through cleaning, analysis, and reporting. The role involves managing data systems, building dashboards, and contributing to donor reporting and programme decisions. It requires strong technical skills in Python or R, attention to data quality in low-resource settings, and the ability to work independently in a small, agile team. SafeStart is a newly founded nonprofit that may outsource some skills and build systems from the ground up.

What you would do

• Maintain and extend the raw and clean data architecture in Supabase (PostgreSQL).
• Oversee the automated data-cleaning layer that processes raw submissions into clean tables.
• Keep SurveyCTO reference data in sync with source files as programmes scale.
• Design and maintain SurveyCTO XLSForms for M&E data collection instruments.
• Create automations to trigger alerts via WhatsApp, email or Teams based on programme factors.
• Manage parallel monitoring tracks for new and existing communities as defined in the M&E plan.
• Contribute to M&E calculations and study design; run ANCOVA-based analyses where appropriate.
• Handle change requests from field staff and update reference datasets accordingly.
• Maintain verification sampling logic for audits and track audit tasks to completion.
• Design data entry and validation workflows for paper-based forms and digital integration.

What they are asking for

• Bachelor’s degree or higher in a quantitative field (e.g., statistics, data science, economics, epidemiology, computer science).
• Proficiency in Python or R, including data manipulation and scripting for pipelines.
• Experience with relational data models and at least one database or data storage platform.
• Strong Excel skills, including complex formulas and multi-sheet workbook design.
• Ability to communicate analytical findings clearly to non-technical colleagues.
• High attention to detail and a systematic approach to data quality.

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