A Data Analyst & Data Engineer passionate about transforming complex data into actionable insights that drive smarter decisions
I’ve built a career dedicated to using data as a force for good whether it’s improving global health outcomes or enhancing operational efficiency.
Data Visualization & Communication
Data Engineering & Big Data Tools
Machine Learning & Predictive Modeling
Programming & Data Manipulation
Statistics & Mathematics
I'm a Business Analyst and Data Engineer specializing in AI & data solutions, ML pipelines, predictive analytics, and automation. I build end-to-end data systems, from source-to-target mapping and pipeline architecture to executive-ready dashboards, using Python, SQL, NoSQL, R, and PHP, with Power BI and Tableau for visualization and cloud platforms including AWS, GCP, and Azure for scalable infrastructure. My experience spans humanitarian data systems at CARE Zimbabwe, public health analytics at ICAP, and most recently investor intelligence and market analytics at Astrum Drive, an aerospace and defense-tech analytics initiative. I focus on turning complex, messy data into decisions leadership can act on and trust
Excellent communication and presentation skills
Strong collaboration with cross-functional teams. Project management and agile workflow understanding. Adaptability and continuous learning mindset
Tools: Power BI, Tableau, Matplotlib, Seaborn, Plotly.
Why it matters: Turning data into clear visuals and insights helps stakeholders make informed decisions
Core concepts: Probability, hypothesis testing, regression, distributions, and statistical inference.
Why it matters: These form the foundation for building accurate models and interpreting data-driven insights.
Languages: Python and R are essential.
Key Libraries: Pandas, NumPy, and SQL for data wrangling and cleaning.
Why it matters: Most of a data scientist’s work involves preparing and exploring data before analysis.
Tools: Scikit-learn, TensorFlow, PyTorch, XGBoost.
Techniques: Regression, classification, clustering, neural networks, and model evaluation.
Why it matters: Data scientists use these to predict outcomes and optimize business decisions.
My thinking process in working with data, starts by cleaning, structuring, and transforming raw data from various sources to make it more suitable for analysis and decision-making. Data is Gold after processing which involves steps like discovery, transformation, validation, and publishing, and is crucial for improving data quality, accuracy, and consistency
“Projects, Roles, and Achievements That Shaped My Data Career”
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