Senior Data Engineer - Singapur, Singapore - Helius

Helius Singapur, Singapore

5 days ago

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Description

Responsibilities

Design and develop data pipelines and data infrastructure to support efficient and scalable data processing and analytics.

Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and translate them into technical solutions.

Implement and maintain data integration and ETL (Extract, Transform, Load) processes, ensuring data quality, reliability, and consistency.

Develop and optimize data storage and retrieval systems, including databases, data lakes, and data warehouses.

Build and maintain data infrastructure automation and monitoring tools to ensure the reliability, availability, and performance of data systems.

Troubleshoot and resolve data-related issues, performance bottlenecks, and data quality problems.

Requirements

Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.

5+ years of experience in data engineering and Data DevOps, including designing, implementing, and maintaining data pipelines and infrastructure.

Proficiency in programming languages such as Python, Java, or Scala, and experience with data processing frameworks like Apache Spark or Apache Beam.

Hands-on experience with cloud data platforms, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure.

In-depth knowledge of SQL and experience with relational databases (e.g., PostgreSQL, MySQL) and distributed storage systems (e.g., Hadoop, S3, or Azure Blob Storage).

Familiarity with data warehousing concepts, data modeling techniques, and data governance frameworks.

Experience with containerization and orchestration technologies like Docker and Kubernetes.

Responsibilities

Design and develop data pipelines and data infrastructure to support efficient and scalable data processing and analytics.

Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and translate them into technical solutions.

Implement and maintain data integration and ETL (Extract, Transform, Load) processes, ensuring data quality, reliability, and consistency.

Develop and optimize data storage and retrieval systems, including databases, data lakes, and data warehouses.

Build and maintain data infrastructure automation and monitoring tools to ensure the reliability, availability, and performance of data systems.

Troubleshoot and resolve data-related issues, performance bottlenecks, and data quality problems.

Requirements

Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.

5+ years of experience in data engineering and Data DevOps, including designing, implementing, and maintaining data pipelines and infrastructure.

Proficiency in programming languages such as Python, Java, or Scala, and experience with data processing frameworks like Apache Spark or Apache Beam.

Hands-on experience with cloud data platforms, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure.

In-depth knowledge of SQL and experience with relational databases (e.g., PostgreSQL, MySQL) and distributed storage systems (e.g., Hadoop, S3, or Azure Blob Storage).

Familiarity with data warehousing concepts, data modeling techniques, and data governance frameworks.

Experience with containerization and orchestration technologies like Docker and Kubernetes.

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