Routiqo

Specialist Data Hub

DKSH

  • Malaysia - Kuala Lumpur
  • Full-time

About the Role

The Data Engineer, Specialist is a key contributor to DKSH's global data capability, responsible for building and sustaining the cloud data platforms that enable data-driven decisions across markets. In this role, you will shape the integrity and scalability of DKSH's data infrastructure, directly supporting business performance and operational excellence on a global scale.

What You Will Deliver

  • Design, develop, and implement end-to-end data pipelines and data integration processes — both batch and real-time — covering data analysis, profiling, cleansing, lineage, mapping, transformation, and the full deployment of Extract, Transform, Load / Extract, Load, Transform (ETL/ELT) solutions.
  • Drive continuous improvements in data quality, reliability, and efficiency by monitoring pipeline performance and optimizing ETL/ELT processes to meet evolving business demands.
  • Establish and deliver best practices across the data management lifecycle, including modular ETL/ELT development, coding and configuration standards, error handling, auditing, and data archival.
  • Identify and implement Artificial Intelligence (AI), automation, and data-driven innovations that streamline operations, reduce manual effort, and measurably improve productivity.
  • Ensure all development aligns with data governance policies and Business Intelligence (BI) platform guidelines, maintaining compliance and consistency across environments.
  • Partner with Information Technology (IT) team members, Subject Matter Experts (SMEs), vendors, and business stakeholders to translate data needs into effective, goal-aligned solutions.
  • Resolve Business-As-Usual (BAU) data issues and change requests efficiently, maintaining thorough documentation of investigations, findings, recommendations, and resolutions.
  • Support production monitoring, operational incidents, and service requests, including standby support as required on a rotation basis within the team.

What You Bring

  • Bachelor's degree in Computing, Information Technology, or equivalent.
  • Fresh graduate or 1-3 year of relevant working experience.
  • Hands-on experience with Microsoft Fabric, including Lakehouse, Warehouse, Data Pipeline, OneLake, Notebook, and deployment pipelines.
  • Practical experience with Azure Data Services, including Azure Synapse Spark, Synapse Database, Azure Data Factory, Databricks, and Azure Data Lake Storage.
  • Solid grounding in ETL/ELT frameworks, data warehousing concepts, data management frameworks, and data lifecycle processes.
  • Proven ability to handle and process structured, semi-structured, and unstructured data effectively.
  • Proficiency in Python, PySpark, and Structured Query Language (SQL) for data engineering development.
  • Experience using Azure DevOps to implement Continuous Integration and Continuous Deployment (CI/CD) workflows and manage ETL job deployments across multiple environments.
  • Strong knowledge of database technologies, including Relational Database Management Systems (RDBMS), NoSQL, and columnar databases.
  • Strong awareness of AI technologies with the ability to apply AI solutions, automation, and analytics to simplify processes and drive operational efficiency.
  • Clear and effective communicator, able to present technical concepts to both technical and non-technical audiences.
  • Self-driven and collaborative, with the ability to work independently and contribute effectively within diverse, multi-stakeholder teams.
  • High sense of ownership, strong affinity for data, and a continuous improvement mindset.
  • Knowledge of SAP is an added advantage.

Why Join DKSH

At DKSH, we help companies grow in Asia and enable people to perform at their best. You will be part of an organization that values accountability, collaboration, and long-term partnerships. We offer a dynamic environment where your contributions are visible and where you can build a meaningful career in Information Technology.

Skills

  • Ai
  • Azure
  • Business development
  • Databricks
  • Logistics
  • SAP
  • Spark
  • Teamwork