Hi, I'm Proud Ndlovu (GitHub: ApostolicDA) — a Data Analyst & Analytics Engineer focused on transforming complex datasets into clear, actionable insights that support strategic decision-making.

My work centers around building end-to-end data pipelines, analytical datasets, and executive dashboards — from cloud data warehousing through transformation layers to BI reporting.

I specialise in the full analytics lifecycle:

Raw Data → BigQuery → dbt Models → Validated KPIs → Executive Dashboards

🔹 Based in Johannesburg, South Africa 🔹 Experienced with SQL, Python, dbt, BigQuery, Power BI, and Looker Studio 🔹 Focused on analytics engineering, business intelligence, and KPI reporting 🔹 Passionate about turning messy operational data into reliable business insights 🔹 Open to remote junior / mid BI, Data Analyst, or Analytics Engineer roles globally

Capabilities

• Data cleaning and transformation
• dbt modelling (staging + marts) on BigQuery
• SQL data modelling & star-schema design
• Exploratory Data Analysis (EDA) & cohort analysis
• KPI development, validation & governance
• Marketing analytics (ROAS, CAC, LTV, Churn)

Capabilities

• Executive dashboards from dbt-modelled data
• KPI reporting frameworks & performance monitoring
• Funnel analysis & cohort retention visualization
• Data storytelling for stakeholders

A production-style analytics engineering project — full stack from cloud warehouse to live executive dashboard.

Stack: BigQuery → dbt (VSCode) → Looker Studio → GitHub

Key Work

• Built staging models (stg_customers, stg_orders, stg_order_items) and mart models (mart_channel_performance, mart_churn_risk, mart_cohort_retention) in dbt on BigQuery
• Engineered channel performance analysis revealing Search as the #1 revenue driver at 2M+ total revenue
• Cohort retention analysis identified sharp first-month drop-off, directly informing early-engagement strategy
• All models version-controlled, documented, and deployed via GitHub with a live Looker Studio dashboard

Skills Demonstrated

dbt BigQuery BigQuery SQL Looker Studio Analytics Engineering Data Modelling Git/GitHub

🔗 View Project on GitHub

End-to-end analytics system integrating multiple admissions datasets into a PostgreSQL warehouse powering a validated BI dashboard.

Key Work

• Integrated 4 operational datasets into a governed analytical model
• Engineered a master dataset using SQL transformations with star-schema design (fact + 3 dimension tables)
• Built a Looker Studio executive dashboard for real admissions decision-making
• Implemented SQL-based KPI validation framework — 100% accuracy across all KPIs and filters

Skills Demonstrated

SQL PostgreSQL Data Modelling ETL Data Governance Looker Studio KPI Validation

🔗 View Project on GitHub

SQL-driven analytics system designed to measure and optimize marketing return on ad spend (ROAS) across campaigns.

Key Work

• Built SQL queries calculating ROAS, CAC, LTV, churn risk, and channel profitability
• Developed multi-table analytical model using CTEs and window functions across 6 relational tables
• Identified channel reallocation opportunities projecting 20–25% ROI improvement
• Created BI-ready datasets supporting executive Power BI performance monitoring

Skills Demonstrated

SQL PostgreSQL Marketing Analytics KPI Modelling CTEs Window Functions Power BI

🔗 View Project on GitHub

Exploratory analytics project investigating fraudulent transaction patterns using Python and SQL-based analysis.

Key Work

• Cleaned and validated 1,056 financial transactions in Python
• Identified 10.61% fraud rate, $38,990 total exposure
• Segmentation revealed 10% of users drove 80% of fraud — directly informing monitoring priorities

Skills Demonstrated

Python Pandas SQL EDA Data Cleaning Risk Analytics Power BI

🔗 View Project on GitHub

⭐ Goal: Build governed data systems and dashboards — from warehouse to insight — that help organizations move from raw data to confident decisions.