Senior Data Engineer

[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