6+ years designing and building data platforms on Azure — specialising in data modelling, lakehouse architecture, and SAP data integration across Synapse, ADLS, and Microsoft Fabric. Currently deepening expertise in distributed computing with Spark and Databricks.
End-to-end medallion lakehouse on Azure — weekly Myanmar job-market scraper (AWS Lambda → S3), ingested via ADF into ADLS Gen2, transformed with Databricks + dbt-core, governed by Unity Catalog. Gold marts track hiring velocity, category demand, and location landscape.
Data Engineer at Schaeffler, where I own the data and analytics infrastructure for the Asia Pacific region. My core focus is on purchasing, supplier management, and finance data — keeping large-scale, multi-country pipelines running reliably as part of a small team of five.
I started as a Data Analyst, which gave me a strong sense of what good data actually looks like from the consumer side. Shifting into engineering felt like a natural progression — I wanted to work closer to the foundation. Data engineers are the ones who make everything downstream possible. No clean pipelines, no reliable analysis, no ML, no reporting. That's the work I care about.
Outside of work I'm going deeper on distributed systems. My thesis explores Spark-based distributed computing for recommendation systems — sitting at the intersection of scale and intelligence, which is where I want to keep pushing. The jobnet-lakehouse project is my hands-on sandbox for cloud-native lakehouse design, built end to end.
When I'm not building pipelines, I play chess, lift weights, and read philosophy. There's more overlap with engineering than it might seem — patience, systems thinking, and being comfortable sitting with hard problems until they make sense.