Data Platform Engineer (สัญญาจ้าง 1 ปี)

Job Responsibilities

• Design, develop, and maintain scalable data pipelines for ingesting, transforming, and delivering data.
• Build and optimize data infrastructure to support analytics, machine learning, and business intelligence.
• Integrate structured and unstructured data from multiple sources into data lakes and data warehouses.
• Develop and maintain ETL/ELT processes, including workflow orchestration and monitoring.
• Ensure data accuracy, availability, reliability, security, and performance across the data platform.
• Implement data quality validation, data lineage, logging, and alerting processes.
• Optimize data processing performance and troubleshoot pipeline or data-related issues.
• Implement CI/CD practices for the development, testing, and deployment of data pipelines.
• Work closely with Data Analysts, Data Scientists, Software Engineers, and business stakeholders.
• Prepare and maintain technical documentation, data models, and pipeline specifications.

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, Data Science, or a related field.
• Experience in Data Engineering, Data Pipeline Development, Big Data, or a related area.
• Proficiency in at least one programming language, such as Python, Scala, or Java.
• Strong SQL skills with databases such as PostgreSQL, MySQL, or SQL Server.
• Experience with ETL/ELT processes and data pipeline orchestration.
• Good understanding of data architecture, data modelling, and data processing concepts.
• Strong analytical, problem-solving, and troubleshooting skills.
• Good communication skills and ability to work effectively with cross-functional teams.

Preferred Technical Skills

• Big Data and Streaming: Apache Spark, Apache Flink, or Apache Kafka
• Data Orchestration and Transformation: Apache Airflow, Prefect, or dbt
• Cloud Data Platforms: AWS Glue, Azure Data Factory, or Google Cloud Dataflow
• Data Warehouses: Snowflake, BigQuery, Amazon Redshift, or Azure Synapse Analytics
• Data Lakes: Amazon S3, Azure Data Lake Storage, or Google Cloud Storage
• Data Lakehouse Technologies: Delta Lake, Apache Iceberg, or Apache Hudi
• Data Modelling: Star Schema, Snowflake Schema, or One Big Table
• CI/CD tools and practices for data pipelines
• Data Quality and Lineage: Great Expectations, OpenLineage, or equivalent tools