Registration of Interest for #AIDA Talent Acceleration Day (Sept 2026) - Senior AI Data Engineer
Singtel Group Singapore
Telecommunications · 10,001+ employees
About the role
You will design, build, and operate scalable data ingestion and transformation pipelines using modern cloud tools like Databricks and Kafka. Additionally, you will lead the implementation of RAG solution stacks and ensure data quality, security, and operational readiness across the platform.
What they look for
Requirements
The role requires a bachelor's degree in a technical field and 5–8 years of experience in data engineering or cloud-scale analytics. Candidates must possess strong hands-on skills in Python, SQL, and Apache Spark, along with experience in building production-grade data pipelines and AI solutions.
Full description
Build AI. Unlock BIG Possibilities with Singtel.
Singtel’s Artificial Intelligence & Data Analytics team, AIDA, is growing its team of AI, data and cybersecurity and technology professionals.
We are inviting experienced AI Data Engineers to register their interest in current and upcoming opportunities.
How You will Make An Impact:
- 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
- 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.
Profiles may be considered for a range of opportunities across AIDA, depending on role availability and the alignment of each applicant's skills, experience, and background with the requirements of the position.
Are you ready to say hello to BIG Possibilities?
Join Singtel to shape what's next and accelerate your career through meaningful work, continuous learning, and real impact.
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