Completed
Data Engineering Fact Table Project
I cleaned raw data with SQL and Python, then modeled it into fact tables ready for analysis.
- SQL
Python
Open to entry-level data engineering roles
Hi, I'm Divyangkumar Patel
I turn raw data into clean, analysis‑ready tables. I clean and transform data with SQL and Python. I model data into fact tables for analytics. I am building a streaming pipeline with Apache Kafka. I deliver trusted data to Power BI reports. I am learning PySpark for data at scale.
I've built SQL and Python transformation projects and Power BI reports, and I'm now building a streaming pipeline with Apache Kafka.
- MSc Computer Science
- AtliQ virtual internship
- 4 hands-on projects
Curious about data. Focused on engineering.
I studied for an MSc in Computer Science at the University of East London, and I'm now moving toward data engineering: designing the systems that collect data, transform it, and make it dependable.
I started on the analytics side, building dashboards in Power BI, which taught me how much every report relies on the transformation work behind it. That pulled me upstream into Python and SQL transformation work and data modeling, and now into streaming with Kafka and distributed processing with Spark, which I'm still learning.
I learn by building. Most of what I know comes from hands-on experiments and projects I keep extending.
Engineering interests
- Data Pipeline Development
- Distributed Data Processing
- Data Warehousing
- Streaming Architecture
- Data Quality
- Cloud Data Platforms
The tools behind the work.
Each group links to the projects where I've used those tools.
- Python
- SQL
AWS
Pandas
NumPy
Apache Spark- PySpark
Apache Kafka- ETL
- ELT
- Data Modeling
- Data Pipelines
Azure
Azure SQL
Snowflake
MySQL
Databricks
Power BI- Power Query
- DAX
Tableau
Excel
Git
GitHub
Jupyter Notebook
VS Code
Programming
The two languages nearly all of my data work is written in.
- Python
- SQL
- AWS
Data Processing
Cleaning, reshaping and analysing data, from a notebook to a cluster.
- Pandas
- NumPy
- Apache Spark
- PySpark
Data Engineering
Moving data between systems and giving it a dependable structure.
- Apache Kafka
- ETL
- ELT
- Data Modeling
- Data Pipelines
Cloud and Databases
Where the data is stored, queried and processed.
- Azure
- Azure SQL
- Snowflake
- MySQL
- Databricks
Analytics
Turning prepared data into reports people can read.
- Power BI
- Power Query
- DAX
- Tableau
- Excel
Development Tools
Everyday tooling for writing, versioning and sharing work.
- Git
- GitHub
- Jupyter Notebook
- VS Code
Selected work.
Practical projects exploring data transformation, business intelligence, and engineering workflows.
Showing 7 projects
In progress
Streaming Data Pipeline
I'm building Python producers and consumers that move events through Apache Kafka.
- Python
- Apache Kafka
Completed
Business Insights 360
A Power BI report on sales, finance and operational performance. I shaped the source data in Power Query and wrote the report's measures in DAX.
- Power BI
- Power Query
- DAX
Completed
Weather Analytics Dashboard
A Power BI dashboard that turns raw weather data into report-ready tables in Power Query, then summarises it visually.
- Power BI
- Power Query
Planned
NYC Taxi Batch Pipeline
A planned batch pipeline for the public NYC TLC taxi trip records: monthly Parquet files cleaned with PySpark, modeled into a star schema, and scheduled with Apache Airflow.
- Python
- PySpark
Apache Airflow- SQL
Planned
Weather ELT with dbt
A planned ELT pipeline that loads daily forecasts from the free Open-Meteo API into Snowflake and transforms them with tested dbt models.
- Python
- Snowflake
dbt- SQL
Planned
UK Property Prices Lakehouse
A planned medallion lakehouse on Azure for HM Land Registry Price Paid Data, the open record of residential property sales in England and Wales.
- Azure
- Databricks
- PySpark
- SQL
How I approach data engineering.
Step 1 of 4
Ingest
Collect data from APIs, databases, and event streams.
Step 2 of 4
Transform
Clean, validate, and reshape raw datasets with SQL and Python.
Step 3 of 4
Model
Organise cleaned data into fact tables that are easy to query.
Step 4 of 4
Deliver
Make trusted data available to reports and downstream systems.
Background and journey.
Professional development
Data Analyst Internship
AtliQ
Virtual internship, 12/01/2026
- Business analysis.
- Data-driven problem solving.
- Business intelligence reporting.
Server
Mildreds, London
Server, 2/01/2025
- I was server
Education
MSc Computer Science
University of East London, United Kingdom
Postgraduate degree, 12/01/2026