Powering the Future with AIDA
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 designing, building, and operating scalable data ingestion, transformation, and serving capabilities across a modern hybrid cloud data platform, ensuring solutions are reliable, secure, reusable, and aligned to enterprise architecture and governance standards.
- Develop and optimise batch and streaming pipelines using cloud tools such as Databricks and Kafka, applying sound engineering practices to ensure performance, resilience, and maintainability.
- Contribute to the delivery of reliable, secure, and high-quality data for analytics, reporting, and machine learning use cases
- Lead the implementation and operationalisation of knowledge base and retrieval-augmented generation solution stacks to support scalable GenAI and agentic use cases across business domains
Make an Impact by:
- Design, build, optimise, and maintain batch and streaming data ingestion pipelines using platforms such as Databricks and Kafka, ensuring scalability, reliability, observability, and alignment with enterprise data architecture standards.
- 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
- Provide technical guidance to engineers and delivery partners on data platform patterns, reusable components, code quality, deployment readiness, and production support practices.
- Lead integration of data from diverse source systems including files, APIs, databases, and streaming platforms, working with source-system owners and consuming teams to define fit-for-purpose ingestion patterns and delivery timelines.
- Help maintain metadata and pipeline documentation for transparency and traceability
- Own production readiness for assigned data and AI platform components, including observability, incident triage, root-cause analysis, release coordination, and continuous improvement of operational runbooks.
- Participate in integrating pipelines with tools such as Microsoft Fabric, Databricks, Delta Lake, and other platform components
- Build and maintain knowledge base and RAG solution on variety of hosting platforms
- Implement and operate knowledge base storage, lifecycle management and embedding/vectorization
- Contribute to automation efforts using version control and CI/CD workflows
- Apply data governance, security, access control, and operational risk policies during solution design and implementation, ensuring pipelines and knowledge platforms meet enterprise compliance requirements.
Skills for Success:
- Bachelor’s degree in Computer Science, Engineering, or a related field
- 5–8 years of experience in data engineering, data platform engineering, or cloud-scale analytics solution delivery, with demonstrated ownership of production pipelines and platform components.
- Proven ability to independently design, build, optimise, and operate production-grade batch or streaming data pipelines, including orchestration, observability, error handling, performance tuning, and operational support.
- Hands-on experience with Python and SQL for data transformation and validation
- Familiarity with Apache Spark (especially PySpark) and large-scale data processing concepts
- Experience with implementing knowledge base and RAG solutions for agentic AI use cases
- Self-starter with strong problem-solving skills and a keen attention to detail
- Able to work independently and lead technical discussions with engineers, architects, product owners, source-system teams, and business stakeholders to translate requirements into secure and maintainable platform solutions.
- Strong documentation and communication skills
- Strong understanding of enterprise data architecture, cloud security, access control, CI/CD, release management, and production operations for data and AI platform solutions.