Senior AI Data Engineer #AIDA
Singtel's new AIDA (Artificial Intelligence & Data Analytics) unit is hiring a Senior AI Data Engineer in Singapore to design, build, and operate batch and streaming data pipelines (Databricks, Kafka, PySpark) on a hybrid cloud data platform, and to lead knowledge base and RAG solution stacks supporting GenAI and agentic use cases across the telecom enterprise.
- Compensation
- Not disclosed
- Location
- Singapore, Singapore
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 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 ownership of production pipelines
- Proven ability to independently design, build, optimise, and operate production-grade batch or streaming data pipelines (orchestration, observability, error handling, tuning, 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 implementing knowledge base and RAG solutions for agentic AI use cases
- Self-starter with strong problem-solving skills and keen attention to detail
- Able to work independently and lead technical discussions with engineers, architects, product owners, and business stakeholders
- 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 platforms
Full description
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An empowering career at Singtel begins with a Hello. Our purpose, to Empower Every Generation, connects people to the possibilities they need to excel. Every "hello" at Singtel opens doors to new initiatives, growth, and BIG possibilities that takes your career to new heights. So, when you say hello to us, you are really empowered to say... "Hello BIG Possibilities".
Powering the Future with AIDA
To lead the next phase of our AI evolution, we've launched a new business unitAIDA-Artificial Intelligence & Data Analytics- a strategic engine driving our transformation designed to scale our AI ambitions with precision and purpose.This marks a pivotal shiftin how we operate, innovate, and serve to embed intelligence into every layer of our business.
AtSingtel, this is more than a technology upgrade. It's a strategic transformation that redefines how value is created across the enterprise core-augmenting human capabilitiesand unlocking entirely new potential. It is a transformation journey by aligningpeople, platforms, and processesunder one cohesive strategy. Our mission is to buildAI literacy, and foster a culture whereintelligence 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 championsinnovation, embracesagility, and putshuman potentialat 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
- 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