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Babban Gona
IT, Data Science
United States
Babban Gona — 'Great Farm' in Hausa — is Africa's largest maize producing entity, farming an area equivalent to 12 Manhattans. We believe everyone deserves the opportunity to prosper, free from poverty and ensuing violence. At the heart of West Africa's rising insecurity are unemployed youth at risk of being recruited to conduct violent acts. Just as oxygen is to fire, so are unemployed youth to insurgencies.
At Babban Gona, we disrupt this cycle. Our innovative technology platform and franchise model makes farming dramatically more profitable for smallholder farmers, doubling yields, nearly tripling net incomes, and turning at-risk youth into successful agricultural entrepreneurs. We have supported over 110,000 farming families, disbursed more than $200M in smallholder loans, and maintained a 99.9% repayment rate. We are now building the team to reach 10 million families by 2030. Transform your career. Join a movement. Know that every report you file, every automated script you write, and every data point you verify contributes to one of the most consequential social enterprises on the planet.
Babban Gona is expanding its Data Operations function to support rapid organizational growth across our hubs. The Data Analyst is a foundational hire in this expansion — the person who ensures the integrity of our operational data and keeps the engine of our field intelligence running cleanly and accurately every single day.
Why this role matters:
This is an ideal role for a sharp, detail-obsessed professional who wants to build deep technical and operational expertise inside one of the most impactful social enterprises in Africa — and grow with the organization as it scales.
Daily & Weekly Data Operations
Perform daily and weekly data audits and reconciliations, ensuring all discrepancies in field reports are identified, investigated, and resolved within agreed timelines.
Clean and record operational data, verifying documentation and coding accuracy before ingestion into central systems.
Manage data tracking for agricultural inputs and yields, following up on outstanding entries and escalating data gaps in line with protocol.
Maintain accurate, well-organized digital records and documentation for all transactions and field activities processed, ensuring full audit readiness at all times.
System & Process Integrity
Maintain the integrity of operational data within our systems—ensuring records are accurate, complete, and consistently formatted.
Identify and escalate any data anomalies, system errors, or process gaps observed during routine operations.
Support Data team process improvement initiatives by documenting procedures and flagging inefficiencies in data collection.
Lead the transition from manual data entry to automated workflows.
Internal Collaboration & Reporting
Respond promptly to internal queries regarding performance metrics, documentation, and field balances.
Assist with the preparation of periodic operational reports and schedules as directed by senior leadership.
Participate actively in team reviews, bringing observations from daily operations to improve the understanding of data flows at the Giwa Hub.
Education: Bachelor’s Degree in Mathematics, Statistics, Computer Science, Economics, or a related quantitative field.
Experience: 1–4 years of work experience in data analysis, business intelligence, or operational reporting.
Technical Proficiency:
Advanced mastery of Excel and Google Sheets (Complex formulas, Power Query, Pivot Tables).
Familiarity with data query/management tools (e.g., SQL, Access, or Business Objects).
Strong aptitude for workflow automation and a passion for learning new tools to streamline functions.
Core Skills:
Excellent quantitative and analytical skills.
Ability to work as a team as well as independently.
Strong interpersonal skills, including written and oral communication.
Leadership & Mindset: Extensive experience in identifying the root cause of issues and creating innovative solutions; comfortable interacting with management-level stakeholders
