Singapore
I'm a Data Engineer with ~5 years of experience building data pipelines and infrastructure that help teams make faster, better decisions. I work across the modern data stack — Python, Snowflake, Spark, Airflow, and cloud platforms (Azure, GCP, AWS) — to design systems that are scalable, maintainable, and trusted by stakeholders. What I deliver: - Ingestion pipelines that bring data from diverse sources (REST APIs, relational databases, flat files) into central platforms - Data models and views that make analysis faster and more intuitive - Automation (CI/CD, Infrastructure as Code) that reduces manual overhead and speeds up deployment I've supported Finance and HR teams with their data needs — from building pipelines to creating SQL views for reporting — while maintaining ~98% on-time delivery SLAs and participating in on-call rotations. I'm looking for a role where I can build, optimize, and scale data systems that empower teams to work smarter.
- Maintained large-scale data ingestion pipelines built in Python and supported legacy Java jobs, resolving production failures and adding fault-tolerant features to minimize downtime. - Built data models and views in Snowflake to improve data accessibility and lower query times for business reporting and analytics. - Leveraged GitHub Actions to automate CI/CD for pipelines and infrastructure with Terraform, enabling faster, repeatable deployments. - Automated dataset reconciliation via Python to assist the migration of datasets, reducing manual checks and maintaining data accuracy across environments
- Built and maintained Power BI reports for various finance teams to meet their operational and reporting needs - Built datasets with billions of records using Microsoft Azure Platform for business analytical driven decisions - Redesigned ELT processes for the finance data warehouse in Snowflake which reduced compute time by 80% - Developed data pipelines for the automation of GrabPay Invoicing for Thailand, Philippines and Singapore, with time savings of 300 man hours each month and ensured country compliance in Thailand and Philippines
A 12-week full-time intensive data science bootcamp that covers topics that include: Clustering Algorithms, Web scraping, API Querying, Natural Language Processing (NLP), Classical Time Series Modelling, Bayesian Regression, Markov Chain Monte Carlo, A/B Testing, Neural Networks, Flask, AWS, Spark
- Developed and maintained Power BI reports for the Asia M&SC finance team and business stakeholders with DAX and SQL - Extract, clean, prepare and model raw data from sources such as SharePoint, Teradata databases and Snowflake - Automated data cleansing process through the use of various Python scripts and SQLite databases - Performed month-end financial close activities, optimized using Power Query (M) in Excel - Developed a cost prediction model using multivariate regression for one of the business divisions.
- Managed the standard costing set up process, ensuring proper financial flows of the SKUs - Maintained pricing master data for Vietnam market - Prepared monthly management reports for the MSGV cluster (Malaysia, Singapore, Vietnam and Export markets) as part of the monthly forecasting process - Achieved 99% success rate for US-sourced SKU conversions on preparing SKUs ready on time - Optimized the Master Data Tracking process, greatly reducing shipment cuts and time spent (by 80%) - Simplified the entire monthly reporting process through the use of Power Query and Power Pivot (achieved time savings of 14 man hours each month)