Data Engineer

About the role

SimplifyNext is a Singapore-headquartered digital engineering firm, with offices in Thailand and Malaysia, working across three converged areas: agentic AI, AI-enabled application modernisation, and intelligent automation.

We bring strong business process consulting together with deep technology skills and modern delivery practices – product-centric, agile and AI-assisted. We build bespoke applications and AI systems, and we go deep on the platforms our clients run on, including Microsoft, AWS, ServiceNow, Databricks and UiPath. That combination of engineering depth and platform depth is what our clients come to us for.

We build and modernise the systems people depend on daily. Our 300+ practitioners are multi-disciplinary by design – business consultants, software engineers, architects, AI engineers and designers working on the same delivery, not handing off between silos. We focus on delivering outcomes for clients, building strong careers for our people, and staying ahead of the technology curve. We hire because we are growing.

Role Purpose

The Data Engineer designs, builds and deploys scalable data lakehouse platforms for our clients - architecting end-to-end pipelines, establishing data quality frameworks, and delivering production-ready solutions that form the foundation of an organisation's data infrastructure. You will work across modern lakehouse stacks (Databricks, Spark, Microsoft Fabric or AWS) alongside architects, AI engineers and business analysts on the same delivery, not in a separate data silo.

What You'll Own

Data Pipeline & Architecture Design

- Design and implement scalable ETL/ELT pipelines on modern cloud data platforms - AWS (Glue, Step Functions, Lambda, S3), Databricks, Spark or Microsoft Fabric.

- Architect and build data lakehouse solutions using open table formats such as Apache Iceberg, Delta Lake or S3 Tables, including schema evolution, partition evolution and ACID transactions.

- Optimise pipelines for performance, cost and reliability at enterprise scale.

Data Quality & Governance

- Define, implement and maintain automated data quality validation frameworks, with metrics and monitoring for accuracy, completeness and consistency.

- Enforce data governance standards, access controls, lineage and PII handling across the platform, appropriate to public sector and regulated client environments.

Application Development & Deployment

- Write production-quality code and deploy solutions on cloud infrastructure, including Government Commercial Cloud (GCC) environments.

- Apply DevOps practices - CI/CD pipelines and infrastructure-as-code (Terraform, CloudFormation or equivalent) - for repeatable and auditable deployments.

AI & Analytics Enablement

- Prepare and serve data for AI and agentic workloads - feature pipelines, retrieval sources and knowledge bases.

- Support analytics and BI teams with reliable, well-modelled datasets and clear semantics.