Cloud Data Engineer

Job description

Addressing challenging data pipeline and engineering problems by discovering information hidden in vast amount of data by leveraging techniques in statistics, machine learning, and data mining. The data scientist will work closely with the business users, project managers, and database engineers to develop sophisticated analytics algorithms that provide actionable insights. The role requires to a trusted adviser to our clients in conceptualizing advanced analytics solutions and enabling their journey in data science.

RESPONSIBILITIES:
• Work closely with clients from various domains to gather requirements and translate those to suitable technical designs.
• Dive into company data to identify sources and features that will drive business objectives.
• Design data infrastructures architecture for ingestion of real-time and batch data streaming using cloud native services
• Work in interdisciplinary teams that combine technical, business and data science competencies that deliver work in waterfall or agile software development lifecycle methodologies.
• Architect, Design and Build the applications and its CICD pipelines using cloud services.
• Investigate all reported problems/errors & initiate amendments & testing so that the system can operate correctly & efficiently. 
• Deploy and monitor Machine Learning Models in production environment.
• Develop and monitor REST API end points for Machine Learning Models deployment.
• Develop analytics dashboards and communicate its findings using PowerBI/Tableau.
• Setup and monitor cloud infrastructure's security and network settings.

REQUIREMENTS:
• Degree / Masters in Information Technology, Computer Science or Engineering, Information Systems or equivalent discipline
• Have strong technical background in Python development.
• Minimum 4 years experience in using cloud native data lake, ETL, data warehouse and databases services such as DataBricks, Snowflakes, Azure Data Factory, Azure Data Lake, Azure Sypnase, AWS S3, AWS RedShift, AWS Kinesis, AWS QuickSight, etc.
• Experience in developing analytics dashboard using PowerBI/Tableau.
• Experience in deploying Machine Learning Models using Docker/Kubernetes into production.
• Experience with model deployment and operationalization (eg KubeFlow).
• Experience with Python programming, Apache Spark (or PySpark) coding capability to operationalize data analytics workflows & processes
• Experience in developing API end points using Web Framework like Django or cloud serverless services like AWS API Gateway and AWS Lambda.
• Experience in setup CI/CD pipeline and familiar with the DevOps tools like Azure DevOps, Travis CI, Ansible, etc.
• Strong working knowledge in Linux and shell scripting.
• Only Singaporeans

Please contact Ranelle Liew at +65 9836 1864 or RanelleL@charterhouse.com.sg for a confidential discussion

EA License no: 16S8066 | Reg no.: R1874529

Only successful candidates will be notified.