Want to know if this job is worth applying to?
South Africa
Hybrid
You've built systems, automated processes, solved technical problems, or turned complex data into something that works better.
Now imagine applying that thinking to finance.
We're looking for someone who sees reconciliation, reporting, planning, data and financial systems as problems to engineer, not simply processes to manage.
You might come from software engineering, data, automation, consulting, ERP, fintech or another technical environment. You don't need to have spent your career in Finance. What matters is that you've built something real, solved a real problem and can show us how you did it.
We'd rather hire a strong technical builder who can learn Finance than a traditional finance professional who has never built anything.
M-KOPA is one of Africa's fastest-growing fintechs, now serving 7 million customers across five markets and having unlocked over $2 billion in credit.
Our Finance function runs on Microsoft Dynamics and Anaplan, increasingly supported by AI tools such as Claude, and we're reaching a point where the opportunity is bigger than simply improving existing processes.
We need to make our finance infrastructure more intelligent, automated, reliable and scalable.
That's where you come in.
You'll sit inside Finance, but operate with an engineering mindset — understanding how our systems, data, processes and controls fit together, identifying where they break down, and building better ways of working.
Why now?
We've grown from 5 million customers to 7 million, with 10 million next in view.
That scale creates increasingly complex challenges across reconciliation, reporting, planning, data integrity, systems integration and financial operations.
We don't want to solve those challenges by simply adding more people to existing processes.
We want to engineer the processes differently.
If a reconciliation takes two days, your instinct should be to ask:
Why does it take two days? What data is missing? What can be integrated? What can be automated? What can be validated automatically? What should a person actually be doing?
If Dynamics and Anaplan produce different numbers, you shouldn't be satisfied with identifying the variance. You should want to understand the underlying data flow, identify the root cause and help fix the system.
If reporting requires repeated manual intervention, you should be thinking about how to redesign the workflow.
And if AI can remove hours of repetitive work, you should be asking how to deploy it safely and effectively — with the right controls and human oversight.
Most Finance Transformation roles are designed for finance professionals who have developed strong technology skills.
We're approaching it from the other direction.
We're looking for someone with strong technical capability who wants Finance to be their domain.
That could mean you're:
A software, data, automation or AI engineer who has built production solutions and wants to apply those skills in Finance.
A technology or transformation consultant who has designed and implemented systems for clients and enjoys solving complex operational problems.
A Dynamics/ERP functional consultant who can go beyond configuration and has demonstrated the ability to build, automate or work with data.
A fintech or scale-up engineer who has worked close to Finance and automated areas such as close, reconciliation, reporting or financial data pipelines.
A Power Platform, automation or AI builder who has turned manual processes into working solutions.
An analytics or data engineer who understands how to make financial data reliable, structured and useful.
The common denominator isn't your previous job title. It's what you've built.
You have hands-on experience creating something that solved a real problem.
It could be:
An automation
An AI/GenAI workflow
An internal tool
A data pipeline
An agent
A reporting solution
An integration
An ERP solution
A Power Platform application
A reconciliation process
A system or workflow that materially improved how people work
It doesn't have to be built in Claude.
It doesn't even have to have been built specifically for Finance.
But it must be real.
You'll be expected to demonstrate something you've built as part of our interview process.
You naturally look beyond the immediate task and ask how the underlying system works.
You understand that a problem with a number may actually be a problem with:
data → integration → process → system → control → output.
You enjoy finding the root cause rather than repeatedly fixing the symptom.
We're particularly interested in experience across areas such as:
Software engineering
Data engineering / analytics engineering
AI and GenAI
Process automation
APIs and integrations
Microsoft Dynamics / D365
Anaplan
Power Platform
Workflow automation
Data modelling and transformation
Business intelligence
ERP systems
You don't need experience across all of these.
Depth in one or more, combined with the ability and curiosity to learn the rest, is more valuable than a checklist of technologies.
You don't need to arrive knowing every accounting concept.
But you do need to be interested in understanding the domain.
You'll need to become comfortable with areas such as reconciliation, reporting, planning, financial data, controls, audit and the systems that support them.
The expectation isn't that you already know everything.
The expectation is that you can learn quickly and then use your technical capability to make the domain work better.
You'll work alongside the Finance team and across our technology ecosystem, identifying opportunities to improve how Finance operates.
Some weeks that might mean investigating why Dynamics and Anaplan disagree on a number and fixing the underlying integration or data flow.
Other weeks it might mean building an AI-powered workflow that reduces a two-day reconciliation to two hours.
You might redesign a reporting process, build an automated control, improve data integrity, create an internal tool, connect systems, or identify where AI can safely remove repetitive manual work.
You'll also work with Finance colleagues who understand the business and accounting requirements deeply.
Your role is not to replace that expertise.
It's to combine it with engineering thinking to build something better.
You'll become the bridge between Finance, Data, Technology and Engineering — someone who can understand the business problem, interrogate the underlying systems and data, and then help build the solution.
We're flexible about where your technical capability comes from.
We're flexible about whether you've worked in Finance before.
We're flexible about whether your AI experience is Claude, another GenAI platform, or something you've built yourself.
We're flexible about whether your background is consulting, fintech, technology, engineering or ERP.
We are not flexible about whether you can build.
If you tell us you've automated something, built an AI workflow, created an integration or solved a complex technical problem, we want to see it.
Bring us something you've built.
That's one of the first things we'll want to explore with you.
This role sits inside Finance, so you'll need to become comfortable with financial concepts, controls and the realities of a live finance environment.
You'll also be working in ambiguity.
There won't always be a perfect specification. Some systems will be live while you're improving them. Some processes will have existed for years for good reasons. And sometimes the hardest part of automation isn't building the solution — it's getting people to trust it.
You'll need to be able to explain technical ideas to Finance professionals, understand why a control matters, challenge existing ways of working constructively and know when automation should not replace human judgement.
If you enjoy taking messy problems, understanding how they work and building something materially better, you'll probably enjoy this role.
Remote or based in any of our operating markets — capability matters more than location.
If you're a technical builder who wants to apply engineering, automation, data and AI to some of the most interesting problems inside a rapidly scaling African fintech, we'd like to meet you.
And please bring something you've built.
At M-KOPA, we empower our people to own their careers through diverse development programs, coaching partnerships, and on-the-job training. We support individual journeys with family-friendly policies, prioritize well-being, and embrace flexibility.
Join us in shaping the future of M-KOPA as we grow together. Explore more at m-kopa.com.
Recognized four times by the Financial Times as one Africa's fastest growing companies (2022, 2023, 2024, 2025 and 2026), named on CNBC's World’s Top Fintech Companies 2025 & 2026 and by TIME100 Most influential companies in the world 2023 and 2024 , we've served over 10 million customers, unlocking $2 billion in cumulative credit for the everyday earners across Africa, in addition to over 10,000 e-bikes financed in Kenya.
Important Notice
M-KOPA is an equal opportunity and affirmative action employer committed to assembling a diverse, broadly trained staff. Women, minorities, and people with disabilities are strongly encouraged to apply.
M-KOPA explicitly prohibits the use of Forced or Child Labour and respects the rights of its employees to agree to terms and conditions of employment voluntarily, without coercion, and freely terminate their employment on appropriate notice. M-KOPA shall ensure that its Employees are of legal working age and shall comply with local laws for youth employment or student work, such as internships or apprenticeships.
M-KOPA does not collect/charge any money as a pre-employment or post-employment requirement. This means that we never ask for ‘recruitment fees’, ‘processing fees’, ‘interview fees’, or any other kind of money in exchange for offer letters or interviews at any time during the hiring process.
Applications for this position will be reviewed on a rolling basis. Shortlisting and interviews will take place at any stage during the recruitment process. We reserve the right to close the vacancy early if a suitable candidate is selected before the advertised closing date.
If your application is successful M-KOPA undertakes pre-employment background checks as part of its recruitment process, these include; criminal records, identification verification, academic qualifications, employment dates and employer references.