Data Engineer
SimplifyNext2 months ago
Singapore, SingaporeMid Level
Responsibilities
- Design and implement scalable ETL/ELT pipelines using AWS, Databricks, Spark, or Microsoft Fabric.
- Architect lakehouse solutions with Apache Iceberg, Delta Lake, or S3 Tables, including schema and partition evolution and ACID transactions.
- Optimize data pipelines for performance, cost, reliability, and enterprise-scale operation.
- Define and maintain automated data quality validation, monitoring, reconciliation, governance, lineage, access controls, and PII handling.
- Write production-quality code and deploy solutions using CI/CD and infrastructure-as-code such as Terraform or CloudFormation.
- Prepare and serve data for AI and agentic workloads, including feature pipelines, retrieval sources, and knowledge bases.
- Deliver well-modeled datasets and clear semantics for analytics and BI teams.
- Document technical designs, support operational handover, and communicate with technical and non-technical stakeholders.
- Monitor production pipelines, respond to incidents, fix root causes, and tune costs as data volumes grow.
Requirements
- A degree in Computer Science, Data Engineering, Information Systems, or a related field.
- At least 4 years of hands-on experience in data engineering, ETL/ELT development, or data platform roles building and operating production pipelines.
- Strong hands-on experience with Databricks, Apache Spark, Microsoft Fabric, or AWS services including Glue, Step Functions, Lambda, S3, or Redshift.
- Proficiency with Apache Iceberg, Delta Lake, or S3 Tables and lakehouse architecture, including schema evolution, partitioning, and ACID transactions.
- Strong SQL skills and proficiency in Python or Scala/Java for data processing and automation.
- Experience designing data models and warehouse or lakehouse layers for analytics, reporting, and AI workloads.
- Experience defining and automating data quality validation, monitoring, and reconciliation.
- Working knowledge of CI/CD, version control, and infrastructure-as-code such as Terraform or CloudFormation.
- Ability to work with business and technical stakeholders and explain technical designs to non-technical audiences.
- Experience delivering Singapore Public Sector data projects in Government Commercial Cloud environments is preferred.
- Cloud or platform certifications such as AWS Certified Data Analytics, AWS Certified Solutions Architect, Databricks Data Engineer, or Azure/Fabric Data Engineer are preferred.
- Exposure to AI/ML workloads, feature pipelines, vector stores, RAG data preparation, or MLOps is preferred.
- Experience with legacy-system data migration, reconciliation, and cutover is preferred.
- Familiarity with governance frameworks and PII handling, including masking, tokenisation, or Presidio, is preferred.
- Experience with Power BI, Tableau, Looker, and BI or semantic layers is preferred.
Categories
Data Engineering
About SimplifyNext
SimplifyNext is a Singapore-headquartered digital engineering and IT consulting firm founded in 2018. It designs and builds intelligent automation, agentic AI systems, and AI-enabled application modernization for enterprises, working across platforms such as Microsoft, AWS, ServiceNow, Databricks, and UiPath. Privately held, the company operates offices in Thailand and Malaysia and has been named among Singapore’s 100 Fastest Growing Companies.