I am an early-career data professional with hands-on experience in ServiceNow data analytics, SQL-based reporting, ETL workflow development, and data modeling. I am currently focused on building practical Data Engineering projects using Python, SQL, Apache Airflow, MySQL, Docker, Kafka, PySpark, Snowflake, and Databricks.

I enjoy working on real-world datasets, designing data pipelines, creating analytical data models, and transforming raw data into structured, business-ready insights.

• Building end-to-end Data Engineering projects
• Designing ETL pipelines and data warehouse models
• Practicing SQL, PySpark, Apache Airflow, and Kafka
• Learning modern lakehouse workflows using Databricks and Delta Lake
• Improving GitHub project documentation for job-ready portfolios

Programming & Analytics:
Python, SQL, Pandas, NumPy

Data Engineering:
ETL Pipelines, Apache Airflow, Kafka, Data Modeling, Star Schema, Data Quality Checks

Databases & Warehouses:
MySQL, PostgreSQL, MongoDB, Snowflake

Big Data & Lakehouse Tools:
PySpark, Databricks, Delta Lake, Docker

Visualization & Reporting:
Power BI, Matplotlib, SQL Reporting

Tools:
Git, GitHub, VS Code, ServiceNow

End-to-end IPL data engineering pipeline using real Cricsheet ball-by-ball cricket data, Kafka, PySpark, Databricks, Delta Lake, and Spark SQL.

Key Highlights:

• Ingested real IPL ball-by-ball JSON data from Cricsheet
• Parsed raw JSON files into structured delivery-level event records
• Implemented Kafka producer to stream ball-by-ball events into Kafka topics
• Verified real-time event flow using Kafka console consumer
• Built Bronze, Silver, and Gold lakehouse layers in Databricks using PySpark
• Applied data quality checks including null validation, duplicate removal, run validation, and team consistency checks
• Created Gold analytics tables for team, batter, bowler, venue, and match-level analysis
• Used Spark SQL to generate final analytical insights

Tech Stack:
Python, Kafka, Docker, PySpark, Databricks, Delta Lake, Spark SQL

End-to-end Brazilian e-commerce ETL pipeline using Python, Pandas, Apache Airflow, MySQL, Docker, and SQL.

Key Highlights:

• Processed real Kaggle Olist Brazilian e-commerce data
• Automated ETL workflow using Apache Airflow
• Created fact and dimension tables using star schema modeling
• Loaded transformed data into a MySQL warehouse
• Added data quality checks and SQL analytical queries
• Designed the project for data warehouse and reporting use cases

Tech Stack:
Python, Pandas, Apache Airflow, MySQL, Docker, SQL, Data Modeling

• Advanced SQL
• PySpark
• Apache Airflow
• Kafka
• Snowflake
• Databricks
• Delta Lake
• Data Warehouse Design
• Cloud Data Engineering Concepts

I am actively building a strong Data Engineering portfolio by working on practical projects that cover:

• Data ingestion
• ETL/ELT pipelines
• Workflow orchestration
• Data modeling
• Data quality validation
• Batch and streaming pipelines
• Lakehouse architecture
• SQL-based analytics

LinkedIn: www.linkedin.com/in/garvvvit
GitHub: github.com/garvvvit
Email: garvitdhiman2002@gmail.com