Jay Shin

Background

About Jay Shin

Data Engineer | Building Scalable Data Products & Driving Impact I am a Data Engineer passionate about architecting and optimizing robust, scalable data solutions that directly fuel product growth and strategic business decisions. My core expertise lies in designing and implementing end-to-end ETL pipelines (Airflow), creating efficient data models, and leveraging big data technologies (Spark, Kafka) to transform massive raw datasets into reliable, actionable assets. My background in engineering and data science has equipped me with a unique ability to not only build the infrastructure but also deeply understand the "why" behind the data. My professional experience has centered on delivering high-impact data products in demanding, fast-paced environments. At Rhombus Power, I owned and scaled data pipelines processing over 500GB of daily geospatial data, achieving 99.9% SLA and reducing data latency from hours to minutes. This work directly empowered critical analytics and enhanced user decision-making efficiency. Core Competencies: Data Engineering: ETL/ELT Pipeline Architecture, Data Modeling (Schema & Dimensional), Data Warehousing (AWS Redshift), Data Quality & Governance, SQL Programming & APIs: Python (Pandas, NumPy), REST API Development (FastAPI, Flask) Big Data Ecosystem: Apache Spark, Hadoop, Kafka, Docker Cloud Platforms: AWS (S3, Redshift, CLI), Google Cloud Platform (GCP) Databases: PostgreSQL, MySQL, NoSQL (MongoDB) I excel in cross-functional collaboration, partnering closely with product managers, data scientists, and software engineers to define metrics, uncover data-driven opportunities, and build solutions that align with the product roadmap. I believe that the best data engineering tells a clear story, enabling teams to build better products and innovate faster. I am actively seeking a challenging Data Engineer role where I can apply my skills to solve complex problems at scale and contribute to a world-class, data-centric organization.