Data Engineer

Roles & Responsibilities

The Singapore Maritime Institute (SMI) is a collaborative initiative launched in 2011 by three government agencies: MPA, A*STAR, and EDB. Its primary role is to strengthen Singapore's maritime sector through research and development initiatives. SMI focuses on developing the port, shipping, and maritime services sectors by fostering partnerships between industry and academia, ultimately supporting Singapore's position as a global maritime knowledge hub.

By strategizing and managing digital products and solutions, the Digital Translation team enhances Singapore's positioning as a leading global maritime hub. Members of this team work across the full product lifecycle, from conceptualisation to delivery, collaborating with cross-functional experts to drive digital transformation in areas such as the Maritime Digital Twin, Plume Simulation and Modelling as well as Arrival Prediction tool.

As Data Engineer, you will be part of the Digital Translation Team to develop digital products applicable for the maritime industry. You will undertake the functions of Data Engineer to support the collection, storage and analysis of data in a scalable, repeatable and secure manner.

Key Responsibilities

•Support stakeholder engagement, development, implementation and maintenance of systems for data collection, storage, access, and analytics at scale.

•Implement data pipelines that draw operational and sensor data from maritime systems such as SG Maritime Data Hub, to harness data science and AI applications.

•Develop and manage continuous improvement of data architecture and ensure alignment with business requirements, data management and governance policies.

•Participate in the evaluation of strengths and weaknesses of possible technologies and recommend solutions that best meet the organization requirements.

•Design, develop, and maintain interactive dashboards that provide insights and data visualizations to support business decision-making.

•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 research, policy-making and operation/process streamlining.

•Support the design and definition of the data architecture framework, standards, and principles, including modelling, metadata, privacy, security, reference data and master data.

•Assist in the development of data pipelines for large datasets such as operational, sensor or unstructured data to:

oEnable the implementation of DSAI models/applications that support business use cases

oPerform data validation and quality control checks

oAutomate regular updating of models and dashboards

oProvide 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.

•Assist in the compilation and maintenance of a data catalogue to foster awareness of data available to support business use cases.

•Assist in the development of data competencies across research institutes to promote a data-driven culture, via workshops and co-development of data products.

•Collaborates with stakeholders to understand business requirements, designs and develops interactive dashboards using data visualization tools, develops and maintains data pipelines, creates and maintains documentation for the dashboards, works with cross-functional teams to ensure data consistency, and stays up-to-date with the latest data visualization techniques.

Qualifications & Skills

•Background in Data Science, Computer Engineering, Electrical Engineering, Information Systems, or other related disciplines that provide proficiencies in data engineering. Fresh graduates are welcome to apply.

•Good coding and scripting capability in Python and SQL.

•Familiarity with data management, data architecture, ETL processes (for both structured and unstructured data sources), and building data pipelines using distributed process frameworks, e.g. Cloudera Hadoop.

•Knowledge or experience in any of the following areas are a plus:

oExperience in data engineering and data architecture design and implementation, including experience in architecting or developing organizational-level enterprise data lakes or data warehouse solutions.

oExperience working with medium to large data processing pipelines, distributed data stores and distributed file systems, especially enterprise data platforms and Big Data tools.

oExperience in cloud platforms used in Government Commercial Cloud, e.g. AWS and Azure

oProficient in data visualization tools such as Tableau, Power BI.

Skills & Other requirements

•Creative and innovative in solving problems.

•Achievement driven and delivery focused while maintaining required quality.

•Team player with strong organisational 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.

Tell employers what skills you have
Tableau
Machine Learning
Azure
Big Data
Pipelines
Hadoop
Data Management
Scripting
ETL
Data Engineering
Python
Data Architecture
Data Science
Metadata
Power BI
Data Visualization

---