Data Engineer

KKP's Data Exchange team is hiring a Data Engineer to enhance how data moves across the organization, including ingestion into the enterprise data platform and system-to-system integrations. The role involves building scalable, resilient pipelines, implementing data quality checks, managing workflow orchestration, and supporting outbound data delivery on Azure. The position partners with stakeholders across business domains to define data exchange requirements and apply data governance and privacy principles. Kiatnakin Phatra Bank delivers banking and capital-market services nationwide, and the team values clean code, curiosity, and shared learning.

The role requires hands-on experience delivering end-to-end data solutions, proficiency in Python and SQL, and strong practical experience with Databricks and Apache Spark. Candidates should have experience with streaming or event-driven pipelines using Kafka, workflow orchestration tools such as Airflow or Azure Data Factory, and cloud-based data platforms on Microsoft Azure, with AWS exposure considered a plus.

Develop and maintain scalable and resilient ingestion pipelines, integrating data from APIs, file systems, and databases into Azure

Implement data quality checks to ensure completeness, accuracy, and consistency across ingestion and delivery processes

Design and manage workflow orchestration to ensure reliable execution, proactive monitoring, and failure recovery

Work with system integration stakeholders to define, document, and maintain data exchange requirements for both inbound and outbound flows

Build reusable and scalable data integration processes that support both ingestion and outbound delivery across the enterprise

Hands-on experience delivering end-to-end data solutions with a foundation in large-scale data processing, data infrastructure engineering, or data modeling

Proficiency in Python and SQL with solid engineering practices including testing, version control, CI/CD, and maintainable code

Strong practical experience with Databricks and Apache Spark for building, optimizing, and operating production-grade data pipelines

Experience with streaming data or event-driven pipelines using Kafka or similar technologies

Familiarity with workflow orchestration tools such as Airflow or Azure Data Factory, and cloud-based data platforms on Microsoft Azure