Company Description Swift Haulage Berhad is Malaysia's fastest-growing fully integrated logistics provider and is consistently ranked as the top haulier in major ports across Peninsular Malaysia by TEU volume. The Group delivers end-to-end logistics solutions, including container haulage, land transportation, warehousing and container depot services, freight forwarding, and green logistics. Swift is a certified Multimodal Transport Operator (MTO) and holds a PETRONAS license, underscoring its compliance and industry recognition. Operations span Malaysia and Thailand with a large and diverse fleet and 1.8 million sq. ft. of warehousing capacity, supported by container depots with a total capacity of 28,500 TEU. Team members contribute to a dynamic logistics environment that supports regional trade and sustainable growth.
Role Description
The Lead Data Engineer will be responsible for designing, building, and managing the company's new enterprise data platform. This is a hands-on leadership role that will establish the foundational data architecture, pipelines, data lake/warehouse environment, and engineering standards required to support analytics, BI, and future AI use cases across the Group.
The role will support business segments including haulage and land transportation, warehousing, workshop operations, and freight forwarding, with an initial focus on revenue growth, cost optimization, operational efficiency, asset utilization, and data-driven decision-making.
The ideal candidate should be technically strong, business-aware, analytical, and comfortable working in a newly established data function where standards, platforms, and operating models are being built from the ground up.
1. Data Platform Architecture and Engineering
- Design and build the Group's modern data platform using a data lake / lakehouse / cloud data warehouse architecture.
- Work with technologies such as Snowflake or equivalent cloud data warehouse, cloud object storage, Apache Airflow, Apache Superset, SQL, Python, and related data engineering tools.
- Define data architecture standards including raw, curated, and business-ready data layers.
- Establish best practices for data ingestion, transformation, orchestration, storage, access control, and monitoring.
- Ensure the platform is scalable, secure, cost-efficient, and AI-ready.
2. Data Integration and Pipeline Development
- Build reliable data pipelines from key operational and enterprise systems, including but not limited to:
- Transport management systems
- Warehouse management systems
- Freight forwarding systems
- ERP and finance systems
- Fleet, GPS, telematics, and fuel systems
- Workshop and maintenance systems
- Customer, vendor, and third-party data sources
- Legacy databases, APIs, and spreadsheets
- Develop ETL/ELT workflows using Apache Airflow or equivalent orchestration tools.
- Automate recurring data loads and reduce manual data preparation efforts.
- Implement pipeline monitoring, logging, alerting, and error handling.
3. Data Modeling and Business Data Products
- Design and maintain enterprise data models and curated data marts for logistics use cases.
- Build reusable datasets for areas such as:
- Shipment and job profitability
- Trip and route performance
- Fleet and asset utilization
- Warehouse occupancy and productivity
- Workshop maintenance cost and downtime
- Freight forwarding margin analysis
- Customer revenue and cost-to-serve
- Billing, claims, and revenue leakage
- Work closely with the Senior Data Analyst to ensure datasets are analytics-ready and aligned with business KPIs.
4. Data Quality, Governance, and Security
- Establish data quality checks, reconciliation processes, and validation rules.
- Define data ownership, data lineage, metadata, and data cataloging practices.
- Implement security controls for sensitive commercial, customer, employee, and financial data.
- Support master data improvement initiatives for customers, vendors, vehicles, drivers, routes, warehouses, products, and cost centers.
- Ensure compliance with internal data governance policies.
5. BI and Analytics Enablement
- Provide clean, governed, and high-performance datasets to support dashboards and analytics in Apache Superset or equivalent BI tools.
- Optimize query performance and ensure BI users can access trusted data efficiently.
- Support the development of semantic layers and standardized business definitions.
- Enable self-service analytics where appropriate.
6. AI and Advanced Analytics Readiness
- Build data pipelines and structures that can support future AI/ML use cases.
- Prepare data for use cases such as demand forecasting, route optimization, anomaly detection, predictive maintenance, pricing analytics, and operational risk alerts.
- Collaborate with internal stakeholders or external partners on AI pilots and proof-of-concepts.
- Promote responsible and practical use of AI tools within the data engineering workflow.
7. Leadership and Stakeholder Management
- Act as the technical lead for the Group Data & Analytics function.
- Manage data engineering vendors, implementation partners, or future internal data engineers.
- Work directly with the CDO, business heads, IT, finance, operations, and external vendors.
- Translate business priorities into data platform and engineering requirements.
- Promote engineering discipline, documentation, version control, and repeatable delivery practices.
