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- Lead the planning, stakeholder engagement, development, implementation and maintenance of systems for data collection, storage, access, and analytics at scale.
- Design and implement data pipelines that draw operational and sensor data from core MPA systems such as SG Maritime Data Hub, , port operation and vessel traffic information systems, that can be harnessed in data science and AI (DSAI) applications.
- Work with system architects and the core data engineering team to ensure the data architecture aligns with business requirements, data management and governance policies.
- Serve as internal consultant to departments on data engineering best practices.
- Profile MPA's DSAI capabilities and initiatives to gain mindshare within the government and industry.
- Collaborate closely with business users and the data science team to identify relevant data that can help to achieve business goals, such as analysis to inform policy-making and operation/process streamlining.
- Develop data pipelines large datasets such as operational, sensor or unstructured data to: Enable the implementation of DSAI models/applications that support business use cases Perform data validation and quality control checks Automate regular updating of models and dashboards Provide secure access to data via common analytical tools such as Tableau
- Work with the data science team to deploy sophisticated analytics programs, machine learning, and statistical methods, e.g. processing of unstructured input data for automated decision making.
- Identify data processes and tasks that can be automated to increase officer productivity.
- Compile and maintain an MPA data catalogue to foster awareness of data available to support business use cases.
- Develop data competencies across MPA to promote a data-driven culture, via workshops and co-development of data products.
- Background in Data Science, Computer Engineering, Information Systems, Business Analytics, or other related disciplines that provide proficiencies in data engineering or data science.
- At least 5 years' of experience in data engineering and data architecture design and implementation. Experience in architecting or developing an enterprise data lake or data warehouse solution is a plus.
- Strong competency in building data pipelines using distributed process frameworks, e.g. Cloudera Hadoop.
- Strong coding and scripting capability, e.g. expertise in Python/R/SQL.
- Strong knowledge and expertise on data management, data architecture, ETL processes (for both structured and unstructured data sources).
- Knowledge or experience in any of the following areas are a plus: Experience in developing near real-time big data infrastructure and deploying high performance production systems to support data science, machine learning and AI algorithms/applications. Experience working with medium to large data processing pipelines, distributed data stores and distributed file systems, especially enterprise data platforms and Big Data tools. Experience in cloud platforms used in Government Commercial Cloud, e.g. AWS and Azure
- Understand business processes well.
- Creative and innovative in solving problems.
- Achievement driven and delivery focused while maintaining required quality.
- Team player with strong organisational and people management skills.
- Experience in collaborating with interdisciplinary teams that combine technical, business and data science competencies.
- Excellent communication skills, both oral and written, with ability to pitch ideas and influence stakeholders.
Lead Engineer - Singapur, Singapore - careers@gov
Description
[What the role is]
Undertake the functions of Principal Engineer in the Statistics & Data Systems (SDS) department to support the collection, storage and analysis of data in a scalable, repeatable and secure manner.[What you will be working on]
[What we are looking for]