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🚀 Olist Modern Analytics Platform

ADLC Stack Tests Quality

Portfolio Scenario • Modern Data Stack

Production-Style Analytics Engineering

Azure Blob → Snowflake → dbt → Power BI → GitHub Actions

A portfolio platform focused on trust, governance, and measurable delivery quality across ingestion, transformation, semantic modeling, and DataOps.

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🏗️ Architecture Preview

Modern Data Stack Architecture

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📊 Platform Metrics

Automated Tests
559
dbt + Source Tests
dbt Models
24
Staging + Marts
Data Volume
1.55M
Rows Processed
Dashboard Load
< 2s
Performance SLA

🎯 What This Project Demonstrates

Enterprise-Grade Analytics Engineering

End-to-end modern data stack implementation with production-quality standards:

✅ **Architecture:** Clear layer boundaries (RAW → STAGING → INTERMEDIATE → MARTS)
✅ **Quality:** 559 automated tests with 100% data quality score
✅ **DataOps:** CI/CD pipelines with automated testing and deployment
✅ **Performance:** Sub-2-second dashboard loads with cost optimization
✅ **Governance:** Row-level security (RLS), data contracts, semantic layer
✅ **Documentation:** Comprehensive docs with screenshots and evidence

🏗️ Technology Stack

☁️
Azure Blob Storage
Centralized data lake for raw CSV/JSON/Parquet files
❄️
Snowflake
Cloud data warehouse with auto-suspend & resource monitors
🔧
dbt Core
Data transformation with star schema modeling & testing
📊
Power BI
Semantic model with RLS, incremental refresh & BPA validation
🤖
GitHub Actions
CI/CD pipelines for dbt tests & SQLFluff linting
🧠
AI-Assisted Dev
GitHub Copilot + ChatGPT with human validation

📚 Documentation Navigator

📋 Core Design Documents

🎯
Business Requirements
KPI definitions, business questions, success criteria
🏛️
Architecture
System design, data flow, layer responsibilities
📖
Data Dictionary
Schema definitions, business rules, grain documentation

✅ Implementation Quality

🧪
Data Quality Framework
559 automated tests, validation strategy, quality gates
⚡
Performance Optimization
Cost controls, incremental refresh, query optimization
🛠️
Engineering Standards
ADLC framework, DataOps, AI-assisted development

📊 BI & Analytics

🧠
Semantic Model
Power BI measures, RLS implementation, DAX patterns
📈
Analytics Insights
Business findings, KPI analysis, recommendations

🗺️ ADLC 5-Phase Journey

Structured Development Lifecycle

This project follows the Analytics Development Life Cycle (ADLC) framework for organized, phase-gated delivery:

Phase Focus Area Key Deliverables Status
Phase 1 Requirements & Planning Business questions, KPI definitions, architecture design ✅ Complete
Phase 2 Data Ingestion Azure Blob setup, Snowflake RAW layer (1.55M rows) ✅ Complete
Phase 3 Transformation dbt models (staging → marts), star schema ✅ Complete
Phase 4 DataOps & CI/CD GitHub Actions, automated testing (559 tests) ✅ Complete
Phase 5 BI & Semantic Layer Power BI semantic model, dashboards, RLS ✅ Complete

🏆 Key Capabilities

What Sets This Project Apart

🔒 Governance-First Design

  • Row-level security (RLS) with dynamic bridge pattern
  • Data contracts enforce schema validation
  • Certified semantic layer prevents metric chaos

🧪 Quality-Driven Development

  • 559 automated tests (85 source + 474 model tests)
  • 100% data quality score validated
  • CI gates prevent bad data from reaching production

💰 Cost-Optimized Architecture

  • Snowflake auto-suspend (60s/300s) saves compute costs
  • Power BI incremental refresh reduces load times
  • Query tagging enables cost attribution

🤖 AI-Accelerated Development

  • GitHub Copilot integration with custom instructions
  • ChatGPT project with full context management
  • Human validation for all AI-generated code

Built with ❤️ using the Modern Data Stack • February 2026