Senior Data Engineer

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. Responsibilities • 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 Requirements • 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