Professional Summary
Cloud Data Engineer and Analytics Engineer who turns operational data into reliable pipelines, decision-ready models, and measurable business outcomes. Builds secure RAG solutions and fine-tunes models for specialized workflows, with private AWS VPC deployments, country-level data residency, and controls aligned to GDPR and applicable US security and privacy standards. Built five production platforms spanning sales, agriculture, construction, talent, and inventory.
Professional Experience
Founder & Lead Data Engineer
2023 – Present
Mavumium Ecosystem — Five Production SaaS Platforms, Botswana
SQLPythonDatabricksWarehousingAWSDockerTerraform
- Designed and orchestrated ETL pipelines ingesting structured data from Excel, CSV, REST APIs, and PostgreSQL databases across five industry verticals — agriculture, construction, retail, professional services, and quotation management.
- Architected a multi-cloud data warehouse strategy connecting AWS Redshift, Google BigQuery, and Microsoft Fabric into a unified analytics layer, enabling cross-industry reporting across all five platforms.
- Provisioned complete AWS and GCP environments using Terraform IaC — VPCs, IAM roles, S3 buckets, Redshift clusters, Lambda functions, and Cloud Functions — reducing environment setup from several hours to under 20 minutes.
- Developed Power BI and Apache Superset dashboards tracking 40+ KPIs across procurement, sales operations, inventory, and revenue, delivering real-time intelligence to business operators.
- Integrated OpenAI GPT-4o into n8n automation workflows for AI-powered document processing, quotation generation, and CRM data enrichment — reducing manual processing time by approximately 90%.
- Secured all five platforms with Supabase Row Level Security, Cloudflare WAF rules, rate limiting, DDoS mitigation, CSP headers, and HTTPS-only enforcement.
- Deployed all applications on Vercel edge network with Cloudflare CDN, achieving sub-second load times globally and approximately 99.9% uptime across all production surfaces.
Cloud Data Engineering & Infrastructure
2022 – 2023
Independent Engineering Projects
PythonSQLTerraformAWSGoogle CloudMicrosoft FabricDocker
- Built modular Python and SQL ETL pipelines with dedicated extraction, transformation, validation, and loading stages, processing data from multiple heterogeneous source systems.
- Implemented Microsoft Fabric for unified analytics across hybrid on-premise and cloud workloads, connecting Azure Data Factory pipelines to downstream Power BI reporting.
- Deployed Terraform modules for reproducible cloud environments across AWS and GCP, enabling consistent infrastructure from development through production.
- Built data pipeline monitoring systems with freshness checks, anomaly detection, automated alerting, and data quality scorecards to ensure pipeline reliability.
Selected Data & Analytics Projects
Outcome: Reduced quotation preparation from hours to seconds and created structured sales and revenue data for pipeline analysis. Built AI document generation, workflow automation, and reporting on a governed PostgreSQL data layer.
Next.jsSupabaseOpenAIVercelCloudflarePostgreSQL
Outcome: Converted fragmented talent and opportunity information into structured, queryable marketplace data, enabling matching, funnel analysis, and evidence-based decisions about labor demand.
Next.jsSupabaseTypeScriptPostHogVercel
Outcome: Turned farm inputs, costs, and local conditions into crop profitability estimates and AI-assisted recommendations, helping users compare scenarios before committing resources.
Next.jsSupabasePythonOpenAIAWS
Outcome: Structured project and cost data to automate pre-engineering and LOD300 BIM workflows, shortening planning cycles and improving the information available for early cost decisions.
Next.jsSupabaseAWSTypeScriptOpenAI
Outcome: Replaced manual stock recording and specialist scanners with real-time mobile inventory events, creating trustworthy data for replenishment, shrinkage monitoring, and operating decisions.
Next.jsSupabaseCloudflarePostgreSQLVercel
Data Mission — Enriching the African Business Ecosystem
The data collected across these five platforms — spanning SME revenue patterns, agricultural yields, construction costs, professional labor market movements, and retail inventory trends — feeds directly into my data engineering and analytics stack. By aggregating, transforming, and modeling this cross-industry data through AWS Redshift, BigQuery, and Microsoft Fabric, I am building a proprietary multi-sector intelligence layer that enriches the African business ecosystem with actionable insights unavailable through any single source: from understanding what crops are most profitable by region, to identifying labor market gaps, to tracking construction cost inflation across project types. This is applied data engineering with real continental impact.