Data Analyst

SafeStart is a nonprofit organisation implementing a Community-Based Chlorine Access (CBCA) model in rural Kano State, Nigeria. Our programme distributes low-cost chlorine solution to households through a network of community-based redemption agents, with the goal of increasing safe water consumption and reducing waterborne disease. The model is backed by a randomised controlled trial. Most proven health solutions don't fail because the evidence is weak. They fail because no one has worked out a practical way to deliver them. That's the gap SafeStart exists to close, starting with chlorine in Kano and growing into an accelerator for other proven interventions. In 2027 we're also testing how CBCA can move into routine government delivery, working with the Kano State Ministry for Local Government and Chieftaincy Affairs (MLGCA). We are recruiting to fill the position below:

Job Title: Data Analyst

Location: Remote (Nigeria or South Africa)

Report to: Co-founder Role Overview This is a technical, hands-on role working at the intersection of field data collection, automated data cleaning, and impact measurement. You will own the data systems that underpin our monitoring and evaluation (M&E) framework - from raw SurveyCTO submissions through to clean datasets, dashboards, and the analytical outputs used in donor reporting and programme decision-making. The role suits a technically strong analyst comfortable in Python or R, rigorous about data quality in low-resource field settings, and confident working independently on a small, high-performing team. Importantly, SafeStart is a newly founded nonprofit that operates nimbly - we seek candidates who are resourceful, entrepreneurial, and comfortable building systems and processes from the ground up. We are not an organisation that wants you to pretend you can do it all. We are flexible in the skills the candidate can bring, and we are comfortable to outsource some skills if required. If you are excellent at statistical analysis but not too comfortable editing our Supabase backend, we can craft the role in this way. Note too that some of what follows is data-engineering work that is largely built already and fairly stable, once the pipeline is running it needs maintenance rather than constant building, so do not be put off by the technical infrastructure items if your strength is analysis and M&E.

Responsibilities

Data Pipeline & Infrastructure: Maintain and extend the raw/clean data architecture in Supabase (PostgreSQL), ensuring SurveyCTO submissions flow correctly through to raw and clean tables. Own the automated data-cleaning layer (a Python cleaner orchestrated through n8n) that validates and transforms raw submissions into clean tables, deploying updates through the existing version-controlled workflow. Keep SurveyCTO reference datasets (communities, agents, staff) in sync with source files as the programme scales. Design and maintain SurveyCTO XLSForms for all M&E data collection instruments, ensuring reliability under poor rural connectivity conditions. Build automations (in n8n) to trigger WhatsApp / email / Microsoft Teams alerts based on set programme factors. M&E System Build & Maintenance: Manage parallel monitoring tracks for new and existing communities as defined in the M&E plan, including process-check-only visits at the specified interim rounds. Contribute to M&E calculations and study design where required. Where skills align, run ANCOVA-based analyses for programme evaluation. Manage the change-request queue: field staff submit requested updates to registered details (agents, leaders, communities); review, approve, and apply the approved changes to the reference datasets. Maintain the verification sampling logic that selects field visits for audit (audio audits, revisit calls, co-visits) at the agreed rates, and track the resulting audit tasks to completion. Design data entry and validation workflows that account for enumerators using paper-based forms, coordinating manual entry into digital systems accurately and on schedule. Data Quality & Analysis: Monitor incoming field submissions for completeness, plausibility, and consistency; flag anomalies to the Fieldwork Manager for follow-up. Produce clean datasets and summary outputs for each M&E survey round. Proactively interrogate data beyond routine reporting - surface patterns, anomalies, or emerging trends that have programme implications, even when not asked. Maintain a changelog for all dataset versions and cleaning rule updates, ensuring reproducibility. Reporting & Communication: Develop and maintain Power BI dashboards and visualisations presenting programme metrics to leadership, implementation partners, and donors. Configure alerts (via n8n to WhatsApp / email / Microsoft Teams) for field staff to surface data-submission issues in near real-time. Contribute data sections of donor reports, programme evaluations, and strategic planning documents.