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Data Engineer with more than 4 years of experience designing, building, and operating scalable data platforms and production-grade data pipelines in cloud environments. Strong background in Spark-based ETL, workflow orchestration, data lakehouse architectures, and infrastructure-as-code. Proven ability to improve pipeline reliability, performance, and observability across batch and event-driven systems. Passionate about long-term ownership, system quality, and platform-level engineering.…Data Engineer with more than 4 years of experience designing, building, and operating scalable data platforms and production-grade data pipelines in cloud environments. Strong background in Spark-based ETL, workflow orchestration, data lakehouse architectures, and infrastructure-as-code. Proven ability to improve pipeline reliability, performance, and observability across batch and event-driven systems. Passionate about long-term ownership, system quality, and platform-level engineering.

Kankavee Chanasit

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Data Engineer with more than 4 years of experience designing, building, and operating scalable data platforms and production-grade data pipelines in cloud environments. Strong background in Spark-based ETL, workflow orchestration, data lakehouse architectures, and infrastructure-as-code. Proven ability to improve pipeline reliability, performance, and observability across batch and event-driven systems. Passionate about long-term ownership, system quality, and platform-level engineering.…Data Engineer with more than 4 years of experience designing, building, and operating scalable data platforms and production-grade data pipelines in cloud environments. Strong background in Spark-based ETL, workflow orchestration, data lakehouse architectures, and infrastructure-as-code. Proven ability to improve pipeline reliability, performance, and observability across batch and event-driven systems. Passionate about long-term ownership, system quality, and platform-level engineering.

Available to hire

Data Engineer with more than 4 years of experience designing, building, and operating scalable data platforms and production-grade data pipelines in cloud environments. Strong background in Spark-based ETL, workflow orchestration, data lakehouse architectures, and infrastructure-as-code. Proven ability to improve pipeline reliability, performance, and observability across batch and event-driven systems. Passionate about long-term ownership, system quality, and platform-level engineering.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Work Experience

AWS Data Engineer at Adastra Co., Ltd.

January 1, 2024 - Present

Designed and enhanced scalable Spark-based ETL pipelines using AWS Glue and Apache Hudi to process large-scale customer data into analytics-ready tables. Optimized PySpark jobs by improving partitioning strategies, execution plans, and error handling, increasing pipeline stability and reliability in production. Built reusable Terraform modules to standardize AWS data infrastructure provisioning and reduce setup time for new pipelines. Implemented monitoring, alerting, and notification systems by integrating AWS resource usage metrics with New Relic to improve pipeline observability and operational reliability.

Remote Data Engineer at Oyssey Co., Ltd.

July 1, 2024 - November 30, 2024

Built and operated Airflow-based ingestion pipelines to retrieve data from external APIs and compute statistical metrics at scale. Pre-processed and transformed geospatial boundary datasets to support map pinning and spatial analytics use cases. Integrated multiple third-party datasets (demographic, geographic, political) to enrich core property data models.

Data Engineer at ZRCH Co., Ltd.

October 1, 2021 - December 1, 2023

Designed, built, and maintained end-to-end data ingestion pipelines for real estate and marketing platforms. Implemented batch- and event-driven architectures using AWS Glue and Apache Pulsar. Integrated third-party APIs and ML-driven enrichment pipelines (POI data, predictions). Supported increasing data volumes by improving pipeline scalability and reliability.