Can you tell me a bit about yourself as a data engineer?

Sure. I started my career as a software developer, where I built small data processing scripts in Python. After a year, I realized my passion lay in building scalable data pipelines, so I transitioned to a data engineering role at a fintech startup. There, I designed and implemented an ETL pipeline that ingested transaction data from multiple sources, transformed it using Spark, and loaded it into a data warehouse on Snowflake. I also set up automated data quality checks and monitoring dashboards in Grafana.

In my most recent position, I led a team of three engineers to migrate our on‑prem Hadoop cluster to a cloud‑native data lake on AWS. We used AWS Glue for cataloging, Lake Formation for security, and Redshift for analytics. This migration reduced query latency by 40% and cut storage costs by 25%.

What drives me is turning raw data into actionable insights. I enjoy collaborating with data scientists and product managers to understand business needs and then architect solutions that are robust, maintainable, and cost‑effective. My goal is to keep learning new tools—like Flink and Kubernetes—so I can build even more efficient data pipelines in the future.