Open to opportunities

Albert Wijaya

@albertwijaya

Message

I build automated data pipelines, dashboards, and data-quality solutions that accelerate business decision-making.

Indonesia

Message

What I'm looking for

I'm looking to build scalable data products that improve data quality, automate manual processes, and give business teams faster access to actionable insights.

At PT Glaxo Wellcome Indonesia, I build reliable datasets, automated data processes, Power BI dashboards, and pipelines that support reporting, performance monitoring, and business analysis.

Previously, I improved data quality and governance through automation, developed customer segmentation and web-scraping pipelines, and helped business teams access timely insights. My work has reduced manual effort and accelerated delivery cycles.

My background spans banking, pharmaceutical, business analysis, and campaign management. I work with Python, SQL, Power BI, Azure Databricks, ETL pipelines, and stakeholder requirements to turn business needs into scalable data solutions.

Experience

Work history, roles, and key accomplishments

Current

Business Intelligence and Data Platform Specialist

Current

PT Glaxo Wellcome Indonesia

Mar 2026 - Present (7 months)

Designed and developed Power BI dashboards and data visualizations to support business needs and performance monitoring. Automated recurring reports and data processes to improve operational efficiency and reduce manual effort.

Power BI Data Visualization Data Pipelines ETL Data Integration Automation

Education

Degrees, certifications, and relevant coursework

Purwadhika Digital Technology School

Bootcamp, Data Science and Machine Learning

2021 - 2022

Completed a Data Science and Machine Learning Bootcamp, gaining practical skills in data analysis and machine learning.

Multi Data Palembang University

Bachelor of Science, Computer Science

2014 - 2018

Grade: 3.67

Pursued a Computer Science major with a GPA of 3.67. Completed an honors project on implementing gradient features to count motor vehicles in traffic video, achieving 84.85% precision and 82.35% recall.