👋 Hi, I’m Dhananjay Hawal

✨ Analytics Engineer
🔧 Data Engineer
📊 Data Strategist — Helping businesses turn data into action for 10+ years
📍 Based in India

I turn messy data into business clarity. Over the past decade, I’ve built pipelines, models, and dashboards that help product leaders and CXOs make faster, smarter decisions. I specialize in:

• Building robust data pipelines and automation workflows
• Developing dbt models and machine learning projects
• Driving product and business metrics with product managers
• Presenting insights and dashboards to leadership and CXOs

🛠️ Tech Stack

• Languages: Python, R, Scala, SQL
• Cloud Platforms: GCP (BigQuery, Cloud SQL, GCS), AWS (basic)
• ETL/ELT: dbt, Airbyte, Fivetran, Apache Airflow
• Databases: MySQL, HBase (NoSQL), PostgreSQL
• Tools: Tableau, Metabase, Looker, Git, Docker, VS Code
• Data Visualization: Tableau, Power BI, Looker Studio, Metabase
• Big Data: Hadoop, HDFS, SQOOP, Hive, HiveQL, Apache Spark, Apache Kafka, AirByte
• Skillsets: Data Engineering, Machine Learning, Business Intelligence

🔗 GitHub Repo Project Notion Page

Tech Stack: OpenWeather API, Google Cloud Composer (Airflow), BigQuery, Cloud Storage, Google Colab, Looker Studio

Highlights:

• Built a real-time pipeline fetching weather data (temp, humidity, conditions) for 15 Indian cities
• Orchestrated ingestion with Cloud Composer (Airflow), ensuring hourly updates
• Stored curated data in BigQuery, partitioned & clustered for cost optimization
• Archived raw JSON to Cloud Storage for backfills & audits
• Created Looker Studio dashboards for temperature trends, humidity & city-wise comparisons
• Performed advanced analysis in Google Colab notebooks

🔗 Project Notion Page

Tech Stack: PostgreSQL (Cloud SQL), Airbyte, dbt, Airflow, Tableau, GCP

Highlights:

• Built a cloud-native ELT pipeline using open-source tools
• Configured IP-restricted Cloud SQL with Airbyte & Xmin CDC
• Created dbt transformations with test coverage
• Orchestrated end-to-end jobs via Apache Airflow (Docker)
• Developed Tableau dashboards on top of dbt models

📊 View Tableau Dashboard

🔗 GitHub Repo

Tech Stack: dbt, Postgres, Airflow, Metabase

Highlights:

• Designed a complete pipeline using the Medallion Architecture (Bronze → Silver → Gold)
  • Bronze: Raw data ingestion into PostgreSQL
  • Silver: Applied cleaning, PII handling, and dbt test coverage
  • Gold: Built dimensional models optimized for analytics & reporting
• Automated orchestration with Apache Airflow (Docker-based setup)
• Delivered insights through Metabase dashboards connected to Gold models
• Fully open-source implementation showing how modern stacks can be built without proprietary platforms

🔗 GitHub Repo

Tech Stack: dbt, Snowflake, Looker Studio

Highlights:

• Modeled raw call center data into staging, intermediate, and marts layers
• Created product & category KPIs (conversion rate, AOV, revenue, rolling 7-day trends)
• Built rep × product performance analysis and funnel metrics
• Implemented dbt tests, source freshness, macros, and SCD2 snapshots
• Documented everything with dbt docs and visualized insights in Looker Studio

I also share my thoughts and learnings on data engineering and analytics through blogs:

1. 📑 ETL vs ELT: Which One Should You Choose?
   A field guide to ETL vs ELT with real scenarios and diagrams (Twitter API, Mixpanel events, dbt, etc.).

2. 📊 Choosing the Right Visualizations in Product Analytics Tools for User Behavior Analysis When to use Insights, Funnels, and Flows in Mixpanel/Clevertap—plus drop-off formulas.

3. 🗄️ Star Schema vs Snowflake Schema: A Hands-On Project with Superstore Data

   Step-by-step walkthrough of designing and populating STAR and SNOWFLAKE schemas in MySQL/Postgres using Superstore orders data, with SQL scripts and GitHub repo. (More blogs coming soon on modern data stack, data modeling best practices, and real project case studies!)

4. 🗄️ Window Functions Demystified

   My Most Used SQL Window Functions — All in One Query. (More blogs coming soon on modern data stack, data modeling best practices, and real project case studies!)

1. 🏬 Optimizing Online Sports Retail Revenue
2. 🎮 Golden Age of Video Games
3. 🌍 International Debt Statistics
4. 👶 American Baby Name Trends
5. 🏫 NYC Public School Test Scores

1. Performance Metrics (No Sklearn)
2. Logistic Regression from Scratch
3. TF-IDF Implementation
4. Predicting Credit Card Approvals

• Tableau Public Profile
• EDA: Haberman Dataset

• 🐍 Python LeetCode Solutions: Explore
• 🗄️ SQL LeetCode Solutions: Explore
• 🧠 SQL Practice (LeetCode + StrataScratch): Explore

✨ These solutions showcase my evolving SQL thinking and best practices.

📩 Let’s Connect:

• LinkedIn
• Tableau Public