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About the Role
The Data Engineer designs, builds, and deploys scalable data lakehouse platforms, architecting end-to-end pipelines, establishing data quality frameworks, and delivering production-ready solutions. You will work across modern lakehouse stacks alongside architects, AI engineers, and business analysts.
Responsibilities
- Design and implement scalable ETL/ELT pipelines on modern cloud data platforms (AWS, Databricks, Spark, Microsoft Fabric).
- Architect and build data lakehouse solutions using open table formats (Apache Iceberg, Delta Lake, S3 Tables), including schema and partition evolution, and ACID transactions.
- Optimize pipelines for performance, cost, and reliability at enterprise scale.
- Define, implement, and maintain automated data quality validation frameworks with metrics and monitoring.
- Enforce data governance standards, access controls, lineage, and PII handling.
- Write production-quality code and deploy solutions on cloud infrastructure, including Government Commercial Cloud (GCC) environments.
- Apply DevOps practices (CI/CD, infrastructure-as-code) for repeatable and auditable deployments.
- Prepare and serve data for AI and agentic workloads.
- Support analytics and BI teams with reliable, well-modelled datasets.
- Work across cross-functional teams and communicate technical designs clearly to non-technical stakeholders.
- Produce thorough technical documentation for handover to Day 2 operations teams.
- Monitor, respond to incidents, perform root-cause fixes, and tune costs for production pipelines.
- Use an AI workbench to accelerate pipeline scaffolding, transformation logic, and test data generation.
- Use an AI workbench to support schema mapping, profiling, and technical documentation of source systems.
- Use an AI workbench to assist with code review, data quality rule suggestion, and runbook drafting.
Requirements
- Degree in Computer Science, Data Engineering, Information Systems, or a related field.
- 4+ years of hands-on experience in data engineering, ETL/ELT development, or data platform roles.
- Strong hands-on experience with at least one modern data platform (Databricks, Apache Spark, Microsoft Fabric, or AWS).
- Proficiency with open table formats and lakehouse architecture (Apache Iceberg, Delta Lake, S3 Tables).
- Strong SQL and proficiency in Python (or Scala/Java) for data processing and automation.
- Experience designing data models and warehouse/lakehouse layers for analytics, reporting, and AI workloads.
- Experience defining and automating data quality validation, monitoring, and reconciliation.
- Working knowledge of DevOps for data (CI/CD, version control, infrastructure-as-code).
- Ability to work directly with business and technical stakeholders and communicate technical designs clearly to non-technical audiences.
- Experience delivering data projects in the Singapore Public Sector, particularly in a Government Commercial Cloud (GCC / GCC+) environment.
- Cloud or platform certification (AWS Certified Data Analytics, AWS Certified Solutions Architect, Databricks Data Engineer, or Azure/Fabric Data Engineer).
- Exposure to AI/ML workloads (feature pipelines, vector stores, RAG data preparation, MLOps).
- Experience with data migration from legacy systems, including reconciliation and cutover.
- Familiarity with governance frameworks and PII handling (e.g., masking, tokenisation, Presidio).
- Experience with BI and semantic layers (Power BI, Tableau, Looker) and enabling self-service analytics.
Skills
- Data Engineering
- ETL/ELT Development
- Data Platform
- Data Lakehouse
- Databricks
- Apache Spark
- Microsoft Fabric
- AWS
- Apache Iceberg
- Delta Lake
- S3 Tables
- Schema Evolution
- Partition Evolution
- ACID Transactions
- SQL
- Python
- Scala
- Java
- Data Modeling
- Data Quality
- Data Governance
- DevOps
- CI/CD
- Infrastructure-as-Code
- Terraform
- CloudFormation
- AI
- Agentic Workloads
- Feature Pipelines
- Retrieval Sources
- Knowledge Bases
- Analytics
- Business Intelligence
- Communication
- Technical Documentation
- Monitoring
- Incident Response
- Cost Tuning
- Government Commercial Cloud (GCC)
- AWS Glue
- AWS Step Functions
- AWS Lambda
- AWS S3
- AWS Redshift
- MLOps
- Data Migration
- PII Handling
- Power BI
- Tableau
- Looker
Location
- Singapore
- Thailand
- Malaysia
Work Type
- Full-time
Experience Level
- 4+ years of hands-on experience
Education Level
- Degree in Computer Science, Data Engineering, Information Systems or a related field
About the Company
- SimplifyNext is a Singapore-headquartered digital engineering firm with offices in Thailand and Malaysia.
- The firm works across agentic AI, AI-enabled application modernisation, and intelligent automation.
- They combine business process consulting with deep technology skills and modern delivery practices.
- SimplifyNext builds bespoke applications and AI systems, focusing on platforms like Microsoft, AWS, ServiceNow, Databricks, and UiPath.
- Their 300+ practitioners are multi-disciplinary, including business consultants, software engineers, architects, AI engineers, and designers.
- The company focuses on delivering outcomes for clients, building strong careers, and staying ahead of the technology curve.
- SimplifyNext is hiring because they are growing.
- The company is committed to building a team of curious, driven, and forward-thinking individuals who care about creating meaningful impact through technology.
- They offer opportunities to grow, collaborate, and shape the future of digital transformation.