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Portfolio and Experience

Pacmann

2021 - now

In Pacmann, I'm working as Lecturer and Data Engineer

Of course building Data Pipeline. Because Data Pipeline is my passion☝️🔥✨🗣️
Nah, but in serious note Data Engineer is fun field for me that really sucks at math. hehe
Because building Data Pipeline, usually I will do requirements gathering to understanding what's stakeholder pain, needs, and give them my solutions
Before implement Data Pipeline, I will discuss with my peers how's the flow to data, data architecture, transformation rules, etc
I've already built Data Pipeline to get data from source like Odoo, Database, and Spreadsheet
After that, the data is transformed then dump it to Data Warehouse that later can be used for Reporting, Dashboarding, ML Model, etc
By using my Data Pipeline, it cut all manual process like get data from database and write it to spreadsheet. It's all automated baby in this pipeline 😎
It's not the best solution because there's still a room to improve. I learn a lot about Data Pipeline, Data Architect, and how to create proper documentation
It's a really fun project for me. Because my data product is used by other team for business decisions! Bangga cuk bisa bikin gituan
Oh yeah, I also create small data pipeline that extract data from database then write it to Spreadsheet that later use by Ops Team to create Dashboard in Looker

Because I'm lecturer, my experience is creating material for Pacmann's students😅
In Pacmann's not only teach about tools, but also teach the "philosophy" and theory why you build this data product
I'm not expect that I helps more than 1000 students in the courses
I forgot, I also become Mentor (lol) and helps more than 100 students by guiding them in their exercises and students side project
Sometimes by request I wrote a Twitter content for @pacmannai in Data Engineering topics like Data Wrangling, SQL, Python, how to build Data Engineer Portfolio, and many more

2023 - now

Obviously it's NDA Stuff, but mainly creating Data Pipeline and Monitoring
Create Data Pipeline to Extract data from Indonesian Law Agencies data source like API, Database, and CDC using Apache NiFi
Data Pipeline can process data roughly 5 millions records in one day
Besides creating Data Pipeline, also designing Data Quality that can be used to tract the quality of the data source before process by Data Pipeline
Cleansing, Transforming, and Mapping values (using Pandas) from Indonesian Army Agencies that later used for Dashboard

Create Data Pipeline (again...) using Python script to scrape the information about Comifuro's Catalog
Write the scraping results to Google Spreadsheet and create a simple Dashboard
Using cron scheduler to run the script everyday