To lead the next phase of our AI evolution, we've launched a new business unit
AIDA
- Artificial Intelligence & Data Analytics - a strategic engine driving our transformation designed to scale our AI ambitions with precision and purpose.This marks a
pivotal shift
in how we operate, innovate, and serve to embed intelligence into every layer of our business.
At
Singtel
, this is more than a technology upgrade. It's a
strategic transformation
that redefines how value is created across the enterprise core--
augmenting human capabilities
and unlocking entirely new potential. It is a transformation journey by aligning
people, platforms, and processes
under one cohesive strategy. Our mission is to build
AI literacy
, and foster a culture where
intelligence empowers people
.
We welcome you to join us
on a transformational journey that's reshaping the telecommunications industry -- and redefining what's possible with AI at its core.
Grow with us
in a workplace that champions
innovation
, embraces
agility
, and puts
human potential
at the heart of everything we do.
Be a Part of Something BIG!
Responsible for building and supporting data ingestion and transformation pipelines in a modern hybrid cloud platform
Develop basic batch and streaming pipelines, working with cloud tools such as Databricks and Kafka under the guidance of senior engineers
Contribute to the delivery of reliable, secure, and high-quality data for analytics, reporting, and machine learning use cases
Responsible for implementing knowledge base and retrieval-augmented generation (RAG) solution stack to support GenAI agentic use cases
Make An Impact By
Build and maintain data ingestion pipelines for batch and streaming data sources using tools like Databricks and Kafka
Perform data transformation and cleansing using PySpark or SQL based on business and technical requirements
Monitor and troubleshoot data workflows to ensure data quality and pipeline reliability
Work closely with senior data engineers to understand platform architecture and apply best practices in pipeline design
Assist in integrating data from diverse source systems (files, APIs, databases, streaming)
Help maintain metadata and pipeline documentation for transparency and traceability
Participate in integrating pipelines with tools such as Microsoft Fabric, Databricks, Delta Lake, and other platform components
Implement and operate data virtualization layer to centralize visibility and control of data across diverse sources
Contribute to automation efforts using version control and CI/CD workflows
Apply basic data governance and access control policies during implementation
Skills to Succeed
Bachelor's degree in Computer Science, Engineering, or a related field
1-3 years of experience in data engineering or data platform development
Proven ability to independently build basic batch or streaming data pipelines
Hands-on experience with Python and SQL for data transformation and validation
Familiarity with Apache Spark (especially PySpark) and large-scale data processing concepts
Self-starter with strong problem-solving skills and a keen attention to detail
Able to work independently while collaborating effectively with senior engineers and other stakeholders
* Strong documentation and communication skills
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