CarePay is a Kenyan company that administers conditional healthcare payments between funders, patients and healthcare providers. Through our M-TIBA platform, CarePay directs funds from public and private funders directly to patients into a "health wallet” on their mobile phone. The use of these funds is restricted to conditional spending at selected health...
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The Data Generalist is an individual contributor in the Data team, operating across the full data lifecycle. In a small, highly autonomous team, this role focuses primarily on data engineering and backend engineering, with some involvement in data science and analytics engineering, emphasizing end-to-end delivery, stakeholder impact and continuous learning.
Data Engineering & Backend Engineering
Maintain and improve the data infrastructure using Snowflake, AWS, Terraform and Kubernetes, always keeping in mind reliability, security and cost
Orchestrate, refactor and troubleshoot
ETL pipelines in Airflow
Develop FastAPI microservices that expose the data products, from simple reports to advanced rules-based and ML systems
Monitor data quality alerts and raise occurring issues to other development teams
Collaborate with the Head of Data on architectural decisions and technical improvements
Analytics Engineering & Data Modelling
Build and maintain analytics models using dbt, following best practices such as star schemas and dimensional modelling
Adapt ETL pipelines to evolving data models
Ensure analytical datasets are reliable, well-documented, and optimised for self-service BI and downstream consumption
Occasionally work on ad-hoc analyses and customer-facing dashboards
Machine Learning & Advanced Analytics
Design, build, and productionise machine learning models
Develop user-facing ML and AI products and proof-of-concepts using tools such as scikit-learn, LLMs, Streamlit and FastAPI
Collaboration & Mentorship
Actively collaborate with other Data team members through pairing sessions and informal mentorship
Share best practices across data, backend and ML engineering
Reach out to other technical leads to clarify backend logic, data semantics, or system behaviour required for data initiatives
Ownership & Stakeholder Interaction
Lead data projects end-to-end with minimal supervision, from requirements gathering to delivery
Proactively spot opportunities for improvements in the data infrastructure and ML/AI adoption
Understand and articulate trade-offs between technical solutions, delivery speed, and stakeholder needs
Translate complex technical concepts into clear, business-oriented language
Occasionally present and explain data products, insights, and deliveries directly to stakeholders
Requirements
3+ years of experience in a data-related role
Proficiency in Python, SQL, dbt and Airflow
Experience building, maintaining, and documenting APIs in Python (preferably FastAPI)
Familiarity with modern DevOps practices – Cloud (AWS preferred), IaC (Terraform), CI/CD, Kubernetes
Familiarity with data science/ML concepts
Strong curiosity and willingness to learn
Good understanding of data tooling landscape, ability to pick a right tool for the job while keeping a pragmatic mindset
Nice to have: Some experience with LLMs/Agentic AI, Streamlit, Frontend development
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