Data Engineer, Location : Tsuen Wan( 5 Days ) Salary : 32-45K ( Negotiable)
We are seeking a skilled and self-motivated Data Engineer to join our team. The ideal candidate will possess strong data engineering and analytic skills, a basic understanding of supply chain business, and the ability to work with large complex supply chain data. The Data Engineer will collaborate with stakeholders to gather business demands, collect and process data, help to build business intelligence dashboards and provide valuable insights to support our digital transformation targets.
Responsibilities:
Responsible for data engineering and analysis in Sales, Manufacturing, Logistics fields.
Discover potential demands and translate requirements into data-driven solutions with stakeholders.
Design, build, and optimize scalable pipelines for ingesting, transforming, and integrating large-volume datasets.
Work closely with machine learning engineer, apps developers to develop whole data analytics system including end-to-end analytical pipeline and machine learning operation.
Ensure data quality, consistency, and real-time monitoring using tools like DBT, 3rd party libraries that can facilitate data validation processes.
Work closely with data team to promote digital transformation.
Requirements:
Academic degree in Data Science, Statistics, Computer Science or a related field.
At least 3+ year IT experiences in data migration or data pipelines projects.
Exposure to containerization/orchestration (Docker, Kubernetes).
Experience of statistical analysis, Linux system, Pyspark, Git and SQL.
Knowledge of ETL tools such as Apache Airflow, DBT.
Familiarity with one of the major cloud platformsAzure (Devops, Databricks, or Data Factory) is a plus.
Good communication skills including spoken, written English and Chinese.
Candidates have Mfg/ MNC/ Trading background is preferred
Machine Learning Engineer, Tsuen Wan , 5 days, salary : 32-45K ( Negotiable)
We are seeking a highly skilled and proactive Machine Learning Engineer to join our team. The ideal candidate will have a strong foundation in machine learning, with hands-on experience in traditional ML, Generative AI, or agentic AI. A basic understanding of supply chain business and expertise in working with large, complex supply chain datasets are essential for success in this role.
The Machine Learning Engineer will be instrumental in developing cutting-edge data-driven models and algorithms. Key responsibilities include creating innovative use cases, developing proof-of-concept solutions, and leading the deployment of Supply Chain AI applications. And work closely with stakeholders to gather business requirements, manage data processing, build interactive business intelligence dashboards, and provide valuable insights to support our digital transformation target.
Responsibilities:
End-to-End AI Solution Ownership: Design, develop, and deploy scalable AI/ML solutions for Sales, Manufacturing, and Logistics domains using Scikit-learn, TensorFlow, or PyTorch. Ensure seamless integration of models into production systems (APIs, cloud services, or edge devices).
Stakeholder-Driven Problem Framing: Partner with business units to translate operational challenges into data/AI requirements. Define KPIs and success metrics for cross-domain initiatives.
Full-Cycle Data & Model Development: Implement robust pipelines for data cleaning, transformation, and feature engineering. Build and optimize models (predictive maintenance, demand forecasting, route optimization). Create interactive Power BI dashboards to communicate insights.
Model Governance: Establish Model Governance frameworks for performance monitoring, bias detection, and interpretability while implementing ML flow-powered CI/CD pipelines for automated model retraining and lifecycle management.
Cross-Functional Collaboration: Work with data engineers, BI engineers, and application developers to establish end-to-end analytical pipelines and machine learning operations.
Effective Communication: Clearly convey complex AI methodologies and results to both technical and non-technical audiences.
Digital Transformation Partnership: Collaborate closely with the data team to drive digital transformation initiatives.
Requirements:
Bachelor's or advanced degree in Mathematics, Statistics, Computer Science, or a related field.
Preferably 3+ years of hands-on experience working on ML/AI projects.
Model Frameworks: Proficiency in popular machine learning frameworks like Scikit-learn, PyTorch, and TensorFlow, with proven experience in environment setup and management.
Technical Expertise:
Strong understanding of statistical machine learning and deep learning techniques, especially for text, graph, and time-series data (e.g., OCR, NLP, sentiment analysis, forecasting).
Experience with large language models (LLMs) is highly desirable.
Experience in model management with familiarity in MLflow for tracking, versioning, and deploying machine learning models.
Containerization & Orchestration: Knowledge of Docker and Kubernetes for application deployment and scalability.
Solution Optimization: Proven ability to enhance existing ML solutions through pre- and post-processing techniques, fine-tuning, performance evaluation, visualization, and testing.
Version Control & CI/CD: Experience with CI/CD pipelines and version control systems like Git.
Data Visualization: Proficiency in Power BI for creating actionable visualizations.
Cloud Platforms: Familiarity with major cloud platforms (e.g., Azure DevOps, Databricks, or Data Factory) is a plus.
Real-Time Data Technologies: Exposure to real-time data technologies such as Apache Kafka, Azure Event Hubs, and KQL is a plus.
Character:
Strong sense of responsibility and ability to work collaboratively as a team player.
A passion for coding, programming, innovation, and problem-solving.
Self-driven with commitment to continuous AI/ML learning.
Good communication skills including spoken, written English and Chinese.
MS SQL Data Analyst Supervisor, Location : Aberdeen ( 5 Days ) Salary : 34-40K ( Negotiable)
Responsibilities:
Design, develop and maintain robust data pipelines to extract, transaction and load data from various sources into our data warehouse for efficient data storage and retrieval
Collaborate with stakeholders to understand data requirements and translate them into technical specifications and data models
Optimize, troubleshoot and automate data flow, and implement data quality processes to ensure data integrity, accuracy, and to facilitate timely analysis
Work closely with financial analysts and supervisors to support their data needs and enable efficient data analysis and operations such as month end closing
Monitor and optimize data warehouse performance, including query optimization, indexing and data partitioning
Identify opportunities for process improvements and provide actionable recommendations for enhancing data collection, analysis and reporting methodologies
Lead and provide technical guidance to junior data analysts, ensuring timely and high-quality deliverables
Handle ad hoc data analysis requests and projects as required
Requirements:
Degree in Computer Science, Information Systems, Mathematics or other related disciplines
At least 5 years of relevant working experience in data engineering or data analysis, with proven track record of designing and implementing data solutions
Preferably with experience and knowledge in ERP systems, accounting and business processes, and business intelligence
Strong proficiency in MS SQL is must and other database technologies, with the ability to design and optimize complex queries
Good written and spoken English and Chinese
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