01
Source
Understand files, APIs, tables, and business definitions before building.
Data Engineer
Experience Alan's data work from source to shipped product.
I design pipeline journeys from raw data to datasets people can trust. Data Analyst fundamentals show up where they matter: clear metrics, useful dashboards, and decision-ready reporting tables.
02
Pipeline
Move data through repeatable ingestion and transformation jobs.
03
Warehouse
Shape facts, dimensions, and marts around clear analytical grains.
04
Product
Deliver dashboards, query examples, and docs that people can actually use.
About
Data engineering as the foundation, analytics as the feedback loop.
Data Engineer portfolio focused on practical pipelines, SQL-first data modeling, and dependable analytics foundations. I also bring Data Analyst basics for dashboards, metrics, and business-facing insights.
IndonesiaOpen to Data Engineer rolesAnalytics-friendly delivery
Skills
Practical stack for building clean data flows.
Programming
Python / SQL / TypeScript basics / Git
Data Engineering
ETL/ELT / Batch pipelines / Data quality checks / Orchestration
Databases
PostgreSQL / MySQL / BigQuery basics / Dimensional modeling
Analytics
Dashboarding / Metric definition / Exploratory analysis / Reporting
Tools
dbt basics / Airflow basics / Docker basics / Power BI / Looker Studio
Projects
Portfolio projects as case studies.
Open a project to see the stack, problem, journey, output, and demo links in its own page.
Data Engineering
Retail Sales Data Pipeline
Daily raw sales files into clean marts for business reporting.
Python / PostgreSQL / SQL / dbt basics
Read case studyData EngineeringWarehouse Modeling for Customer Analytics
A warehouse model for retention, repeat purchase, and cohorts.
SQL / Dimensional modeling / Data marts / PostgreSQL
Read case studyAnalyticsOperations Dashboard Starter
A lightweight BI layer built on top of prepared reporting tables.
SQL / Power BI / Data visualization / Metric design
Read case studyTimeline
A roadmap toward production-minded data work.
Now
Building Data Engineering Portfolio
Developing practical projects around ingestion, transformation, modeling, and analytics delivery.
Foundation
Data Analyst Basics
Comfortable with SQL exploration, dashboard thinking, KPI definitions, and stakeholder-friendly reporting.
Next
Cloud and Orchestration Practice
Expanding into scheduled workflows, cloud warehouse patterns, data quality, and production-minded documentation.