Data Engineer

Responsible for implementing robust data pipeline using Azure Stack

Responsible for creating reusable and scalable data pipelines

Responsible for using Databrick to build model in Datalake/Azure Synapse

Work with the leadership to set the standards for software engineering practices within the machine learning engineering team and support across other disciplines

Data engineers’ provision and set up data platform/Streaming data technologies that are on-premises and in the cloud.

Manage and secure the flow of structured and unstructured data from multiple sources.

The data platforms include relational databases, nonrelational databases, data streams, and file stores.

Knowledge of Big Data technologies, such as Spark, Hadoop/MapReduce is desirable but not essential

Knowledge of Azure services like HD Insight, Azure DataBricks etc.

Working knowledge of the pros, cons and usages of various ML/DL applications (such as Keras, Tensorflow, Python scikit learn and R)

Contribution to industry/open source communities.

Knowledge and practical experience of cloud-based platforms and their ML/DL offerings (such as Google GCP, AWS, and Azure) would be advantageous

Understanding of infrastructure (including hosting, container-based deployments and storage architectures) would be advantageous

Knowledge on Linux, Iseries, Windows

Qualifications

University degree in Computer Science, Engineering and/or a technically oriented field

2 years of experience with Flink/Spark, Databricks and ideally Kubernetes

2 years of experience with Azure (DP200 and/or DP201, DP203 certification acts as a plus)

Good knowledge of SQL and excellent coding skills (Python)

Passionate about analytics machine learning technology & applications and eager to learn

English communication.

13th month salary, social insurance

Opportunity to work on new technologies and tools

Opportunity to work with multicultural environment and travel.

Performance review and adjust salary twice a year