Data Science Engineer

COMPANY BACKGROUND

Haier is the number one brand of Major Appliances in the world with 16.3 percent of global market share. Headquartered in Qingdao, Haier has over 80,000 employees across 30 countries in the world.

Haier Singapore is one of the holding companies under Haier Group. As the integrated regional centre, Haier Singapore is the main platform to provide Trading, R&D, Big Data, Cybersecurity, Internal Audit and Legal supports to over 80 countries in Asia, Europe, Africa and North America markets.

As the continuous demands from global businesses, we are looking for passionate Data Engineer to join our global big data team.

KEY RESPONSIBILITIES

Based in Singapore, you will be part of the global big data team and responsible for the development, maintenance, integration, data governance and data reporting of Haier’s overseas business systems, including:

Data Integration and ETL:

• Design and develop ETL (Extract, Transform, Load) processes using Azure Data Factory, Databricks & SSIS.

• Integrate various data sources such as databases, APIs, and file systems into a unified data platform.

• Ensure data quality, consistency, and accuracy during the data integration process.

Data Storage and Management:

• Set up and manage data storage solutions using Azure Data Lake Storage, Azure Blob Storage, and Azure SQL Database.

• Implement data partitioning, indexing, and optimization strategies for efficient data retrieval and storage.

• Monitor and maintain data storage infrastructure to ensure reliability and performance.

Data Processing and Transformation:

• Develop and maintain data processing pipelines using Databricks and Spark.

• Implement data transformation logic to convert raw data into meaningful insights.

• Optimize data processing workflows for performance and scalability

Data Analysis and Reporting:

• Collaborate with data scientists and analysts to develop data models and analytical solutions.

• Create and maintain interactive dashboards and reports using tools like Power BI.

• Perform ad-hoc data analysis to support business decision-making. 

AI/Machine Learning Model Development

• Develop, train, and optimize machine learning, deep learning, and generative AI models

• Select appropriate algorithms, features, and architectures based on business problems

• Perform model evaluation, tuning, and validation to ensure accuracy, robustness, and fairness

Security and Compliance:

• Implement security best practices to protect data at rest and in transit.

• Ensure compliance with data privacy regulations and organizational policies.

• Monitor and audit data access and usage to detect and respond to security incidents.

Collaboration and Communication:

• Work closely with cross-functional teams including data scientists, software engineers, and business stakeholders.

• Communicate technical concepts and project status to non-technical stakeholders.

Performance Monitoring and Optimization:

• Monitor the performance of data pipelines, storage solutions, and analytical workloads.

• Identify and resolve performance bottlenecks to ensure efficient data processing.

• Implement automation and orchestration tools to streamline data workflows.

Continuous Improvement:

• Stay updated with the latest trends and advancements in Azure and Databricks technologies.

• Evaluate and recommend new tools and technologies to improve data engineering processes.

• Participate in training and development activities to enhance technical skills.

Documentation and Knowledge Sharing:

• Maintain comprehensive documentation of data pipelines, processes, and architectures.

• Share knowledge and best practices with the team through presentations, workshops, and documentation.

• Ensure that all data engineering activities are well-documented and easily accessible.

These responsibilities ensure that the data infrastructure is robust, scalable, and capable of supporting the organization’s data needs.

KEY REQUIREMENTS

The ideal candidates we are looking for should be seasoned Business Intelligence & AL/ML Professionals with:

• Bachelor's or Master's degree in Data Science, Information Technology, Engineering, or a related field.

• With extensive experience in business intelligence, AI/ML (LLM) and object-oriented development within a commercial environment.

• Familiar with Hadoop (Cloudera distribution), Hive / Impala, Spark, Python, & R.

• Experience in cloud platforms (Microsoft Azure and AWS cloud services), RESTful APIs and microservices

• Experience in databases (Azure, Oracle, SQL Server, MySQL) and enterprise ERP/CRM (SAP, MS Dynamics 365, Salesforce)

• Able to demonstrate solid business understanding in complex B2B and B2C sales environment.

• Good people skills and able to work with people at all levels.

• Strong communication, presentation and interpersonal skills.