Senior Data Engineer

sggovterp; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Housing & Development Board (HDB) seeks a skilled Data Architect to design scalable cloud-native data architectures and lead data pipeline initiatives. You will build robust ETL/ELT processes, drive Data Lakehouse solutions, and collaborate with stakeholders to translate business needs into effective technical specifications.

Ideal candidates have a solid background in SQL, Python, Apache Spark, Kafka or Airflow, and hands-on experience with cloud IaC and security practices.

Qualifications

• Bachelor's degree in a relevant field (e.g., CS, IT, CE).
• Minimum 3 years of experience in data systems architecture and production-scale pipelines.
• Strong understanding of cloud computing, IaC, containerisation, microservices, and security frameworks.
• Proven ability to translate business requirements into technical solutions.

Responsibilities

• Design scalable data architectures on cloud platforms with high availability and security.
• Lead development of Data Lakehouse solutions and ETL/ELT pipelines.
• Collaborate with stakeholders to convert requirements into technical specs.

Skills

SQL

Python

Apache Spark

Data Pipelines

Cloud Computing

Multi-cloud

Education

Bachelor's degree

Tools

Terraform

Apache Kafka

Airflow

AWS

SageMaker

Job description

[What the role is]

The mission of Housing & Development Board (HDB) is to provide affordable, quality housing and a great living environment where communities thrive. To achieve its mission, HDB aims to be data-driven to the core and adopt evidence-based decision making in developing better policies, improving service delivery, and optimising operations.

[What you will be working on]

Data Pipeline Infrastructure & Architecture

• Design and implement scalable data architectures on cloud data platforms with high availability, security, and performance
• Lead development of Data Lakehouse solutions
• Collaborate with stakeholders to understand requirements and translate them into technical specifications

Pipeline Development & Optimisation

• Build and maintain robust ETL/ELT pipelines using modern data engineering tools and frameworks
• Optimise data processing workflows for performance, cost-effectiveness, and reliability
• Implement automated data quality checks and monitoring systems to ensure data integrity

Data Systems Architecting & Solutioning

• Design and architect comprehensive cloud-native Data & AI solutions aligned with business objectives and technical requirements
• Lead cloud migration strategies and oversee implementation of complex multi-cloud environments
• Drive innovation through integration of Data & AI capabilities into HDB's Data & AI platform product architectures
• Conduct technical assessments and recommend modernised approaches using cloud native technologies
• Maintain architectural documentation

Cloud Platform Operations

• Leverage Cloud Native Services to build and manage data infrastructure
• Implement infrastructure as code practices using Terraform
• Ensure compliance with security standards and data governance policies

Technical Leadership & Collaboration

• Mentor junior data engineers and provide technical guidance on complex challenges
• Participate in architectural reviews and contribute to data strategy evolution

[What we are looking for]

• Bachelor's degree in computer science, Information Technology, Computer Engineering, or related field
• Minimum 3 years of relevant experience in data systems architecture, data systems integration, and data pipeline setup at production scale
• Good understanding of cloud computing principles including infrastructure as code, containerisation, microservices architecture, cloud security frameworks, identity and access management, network architecture, and distributed systems
• Proven ability to translate business requirements into technical solutions
• Excellent communication skills for presenting complex concepts to diverse audiences
• Experience with cloud security frameworks, compliance requirements, and risk management
• Experience in data domains (e.g. DataOps , Data Lakehouse) and AI/ML Domains (e.g. MLOps , LLMOps )
• Strong Knowledge and Hands-on experience with SQL, Python and Apache Spark
• Hands-on experience with Apache Kafka, Airflow, or similar technologies

Good to Have:

• Proficiency in Amazon Web Services (AWS) services
• Relevant cloud certifications (e.g. AWS Solutions Architect Professional, AWS Data Engineer Associate) would be an advantage
• Experience with Data & AI cloud-native services (e.g. Amazon SageMaker Unified Studio, Amazon Quick Suite, AWS S3, AWS Glue, AWS Lake Formation, AWS Bedrock, AWS Agent Core).
• Familiarity with serverless computing, edge computing, and IoT architectures would be an advantage .
• Experience with machine learning operations ( MLOps ) and ML model deployment pipelines
• Knowledge of data governance frameworks and metadata management tools
• Familiarity with data visualisation tools and business intelligence platforms