Data and ML Platform Engineer

Data and ML Platform Engineer

Purpose of the Job

To design, build, and maintain scalable data pipelines and architecture that enable reliable, high-quality data availability across port operations. The role will be critical in integrating operational systems such as Navis N4 Terminal Operating System, IoT platforms, and enterprise systems to support analytics, reporting, and AI-driven decision-making across Group RSGT.

Reporting To

Data Science Senior Manager

Working Conditions

Within the terminal – both indoor and outdoor.

Required Experience

• 3–6 years of experience in data engineering or backend data systems.
• Experience working with operational or industrial datasets preferred.
• Proven ability to build and maintain data pipelines in production environments.

Education Requirements

Bachelor's or master's degree in computer science, Data Engineering, Information Systems, or a related field.

Skills / Attributes

• Strong SQL and data modelling expertise.
• Proficiency in Python for data engineering (ETL, pipelines).
• Experience working with APIs and large datasets.
• Understanding of cloud platforms (Azure preferred).
• Strong problem-solving and system design skills.

Key Accountabilities / Responsibilities

1. Data Engineering and Integration

• Design and develop data pipelines to ingest data from operational systems including Navis N4, equipment systems, and enterprise platforms.
• Build ETL/ELT processes for batch and near real-time data ingestion.
• Integrate structured and unstructured data sources across terminal operations.
• Ensure seamless data flow between operational systems and analytics platforms.

B. Data Architecture and Management

• Develop and maintain scalable data models to support reporting and analytics.
• Support implementation of data warehouse / data lake architecture.
• Optimize database performance and ensure efficient query execution.
• Maintain metadata, data lineage, and documentation standards.

C. Data Quality and Governance

• Ensure data accuracy, consistency, and completeness across datasets.
• Implement data validation, monitoring, and quality control mechanisms.
• Collaborate with IT and Data Science teams to enforce data governance standards.
• Support compliance with data security and regulatory requirements.

D. Analytics Enablement

• Prepare clean, structured datasets for use in dashboards and analytics tools such as Microsoft Power BI.

• Enable data availability for key use cases such as:
• Crane utilization tracking.
• Vessel turnaround analysis.
• Yard optimization.
• Support Data Science team in model development and deployment.

E. Performance and Continuous Improvement

• Identify opportunities to automate manual data processes.
• Improve pipeline efficiency and reduce data latency.
• Support scalability of data infrastructure as business needs evolve.

Competencies:

• Analytical Thinking
• Problem Solving
• Collaboration & Influence
• Results Orientation
• Innovation
• Data-Driven Decision Making
• Integrity
• Dynamic