Lead Data Engineer

Singtel Group; Singapore; On-site; SGD 140,000 - 230,000; Full time

Job summary

Singtel Group is building an AI-first telco and seeks an experienced Data Platform Architect to lead the architecture for enterprise-scale data platforms, enabling AI, analytics and autonomous network use cases across cloud-native and on-premise environments.

You will establish reusable data products, drive DataOps maturity and collaborate with product, network and EA stakeholders to translate strategy into a practical data platform roadmap.

Qualifications

• Bachelor's degree in Computer Science, Data Engineering, or a related technical discipline, or equivalent practical experience.
• Min 8 years of relevant experience in data engineering, software engineering or data platform engineering, including substantial experience leading solution design or technical delivery.
• Strong proficiency in Python and SQL; working knowledge of Java and/or Scala is beneficial.
• Strong experience with distributed data processing and streaming technologies such as Apache Spark, Kafka and/or Flink.
• Experience designing cloud-native data platforms using one or more major cloud ecosystems and modern lakehouse/data warehouse technologies.
• Hands-on experience with container platforms, CI/CD and infrastructure-as-code, for example Kubernetes, Docker and Terraform.
• Strong understanding of data modelling, data quality, metadata, lineage, security and governance principles.
• Ability to communicate architecture decisions clearly to engineering teams and senior stakeholders, and to influence across organisational boundaries.

Responsibilities

• Lead the architecture and evolution of enterprise-scale data platforms for AI, advanced analytics and Autonomous Network use cases.
• Establish reusable data products and platform services that reduce one-off integrations and accelerate delivery across teams.
• Raise engineering maturity through DataOps, automated testing, observability, CI/CD, infrastructure-as-code and AI-assisted engineering practices.
• Strengthen trust in enterprise data through data quality, lineage, metadata, security and governance-by-design.
• Own the technical architecture for batch, streaming and event-driven data processing across cloud-native and on-premise environments.
• Design and lead implementation of scalable data ingestion, transformation, orchestration and serving patterns for structured, semi-structured and streaming data.
• Define engineering standards, reference architectures, reusable frameworks and design patterns for data products and platform services.
• Enable AI and GenAI workloads through production-grade datasets, feature pipelines, retrieval data flows, vector stores and related data services where appropriate.
• Drive DataOps practices including automated testing, deployment, monitoring, lineage, data quality checks and operational observability.
• Lead architecture and code reviews, resolve complex engineering trade-offs, and guide teams on performance, resilience, security and cost optimisation.
• Partner with product, network, AI and enterprise architecture stakeholders to translate strategic outcomes into an executable data platform roadmap.
• Evaluate emerging technologies and recommend adoption based on measurable value, interoperability, operational fit and total cost of ownership.
• Coach and mentor engineers, build technical capability and promote a strong engineering culture focused on ownership, reuse and continuous improvement.

Skills

Python

SQL

Leadership

Architecture discussions

Education

Bachelor's degree in Computer Science/Data Engineering

Tools

Kubernetes

Docker

Terraform

Apache Spark

Kafka

Flink

Job description

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We're building an AI-first telco for the future—connecting people, businesses and communities through trusted networks, intelligent digital services and next-generation technology.

From transforming everyday experiences in mobile, broadband, entertainment and lifestyle services to enabling enterprises with AI, 5G+, cloud, cybersecurity, data centres and digital platforms, we create the technology that powers how millions live, work and connect.

At Singtel, your work creates real impact. You'll solve meaningful challenges, shape critical digital infrastructure and help build secure, resilient and AI-enabled solutions that serve businesses, digital ecosystems and communities across the globe.

This is where BIG Possibilities become reality. Whether you're engineering intelligent networks, advancing sovereign AI cloud platforms, strengthening cybersecurity, designing seamless customer experiences or driving sustainable innovation, you'll be part of teams shaping the future of technology.

Backed by decades of expertise and driven by a culture of continuous learning and innovation, Singtel offers the scale, opportunities and support to help you grow your career while making a difference where it matters most.

How You will Make An Impact:

• Lead the architecture and evolution of enterprise-scale data platforms for AI, advanced analytics and Autonomous Network use cases.
• Establish reusable data products and platform services that reduce one-off integrations and accelerate delivery across teams.
• Raise engineering maturity through DataOps, automated testing, observability, CI/CD, infrastructure-as-code and AI-assisted engineering practices.
• Strengthen trust in enterprise data through data quality, lineage, metadata, security and governance-by-design.
• Own the technical architecture for batch, streaming and event-driven data processing across cloud-native and on-premise environments.
• Design and lead implementation of scalable data ingestion, transformation, orchestration and serving patterns for structured, semi-structured and streaming data.
• Define engineering standards, reference architectures, reusable frameworks and design patterns for data products and platform services.
• Enable AI and GenAI workloads through production-grade datasets, feature pipelines, retrieval data flows, vector stores and related data services where appropriate.
• Drive DataOps practices including automated testing, deployment, monitoring, lineage, data quality checks and operational observability.
• Lead architecture and code reviews, resolve complex engineering trade-offs, and guide teams on performance, resilience, security and cost optimisation.
• Partner with product, network, AI and enterprise architecture stakeholders to translate strategic outcomes into an executable data platform roadmap.
• Evaluate emerging technologies and recommend adoption based on measurable value, interoperability, operational fit and total cost of ownership.
• Coach and mentor engineers, build technical capability and promote a strong engineering culture focused on ownership, reuse and continuous improvement.

Skills for Success:

• Bachelor's degree in Computer Science, Data Engineering, Software Engineering or a related technical discipline, or equivalent practical experience.
• Min 8 years of relevant experience in data engineering, software engineering or data platform engineering, including substantial experience leading solution design or technical delivery.
• Strong proficiency in Python and SQL; working knowledge of Java and/or Scala is beneficial.
• Strong experience with distributed data processing and streaming technologies such as Apache Spark, Kafka and/or Flink.
• Experience designing cloud-native data platforms using one or more major cloud ecosystems and modern lakehouse/data warehouse technologies.
• Hands-on experience with container platforms, CI/CD and infrastructure-as-code, for example Kubernetes, Docker and Terraform.
• Strong understanding of data modelling, data quality, metadata, lineage, security and governance principles.
• Ability to communicate architecture decisions clearly to engineering teams and senior stakeholders, and to influence across organisational boundaries.

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.