บริษัท โปรเฟสชันแนลวัน จำกัด

ดูรายละเอียดบริษัท

18 ก.ย. 67

Data Engineer (บึงกุ่ม/พัฒนาการ)

สถานที่ปฏิบัติงาน

เขตพระโขนง กรุงเทพมหานคร

เงินเดือน

90,000-140,000

อัตรา

สัมภาษณ์งานออนไลน์

สมัครงาน

บันทึกงานที่สนใจ

พิมพ์

แชร์

รายละเอียดงาน

Key Responsibilities: 1. Design, develop, and maintain scalable data pipelines for batch and real-time processing 2. Implement ETL processes to extract data from various sources and load it into data warehouses or data lakes 3. Optimize data storage and retrieval processes for improved performance 4. Collaborate with data scientists and analysts to understand their data requirements and provide appropriate solutions 5. Ensure data quality, consistency, and reliability across all data systems 6. Develop and maintain data models and schemas 7. Implement data security measures and access controls 8. Troubleshoot data-related issues and optimize system performance 9. Stay up-to-date with emerging technologies and industry trends in data engineering 10. Document data architectures, pipelines, and processes

คุณสมบัติผู้สมัคร

Bachelor's degree in Computer Science, Engineering, or a related field
2-4 years of experience in data engineering or similar roles
Strong programming skills in Python, Java, or Scala
Proficiency in SQL and experience with relational databases (e.g., Databrick, PostgreSQL, MySQL)
Experience with big data technologies such as Hadoop, Spark, or Hive
Familiarity with cloud platforms (AWS, Azure, or GCP) and their data services
Knowledge of data warehousing concepts and ETL best practices
Experience with version control systems (e.g., Git)
Understanding of data modeling and database design principles
Solid problem-solving skills and attention to detail
Good communication skills and ability
Preferred Qualifications:
1. Experience with Azure data platform (ADF, Databrick)
2. Familiarity with data visualization tools (e.g., Tableau, Power BI)
3. Knowledge of stream processing technologies (e.g., Kafka, Flink)
4. Experience with containerization technologies (e.g., Docker)
5. Understanding of machine learning concepts and data science workflows
6. Familiarity with data governance and compliance requirements
ึ7. Good command in English.