Lead/Senior Research Engineer, ARTC

A*STAR RESEARCH ENTITIES; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

A*STAR RESEARCH ENTITIES in Singapore seeks a data engineer to design and maintain data pipelines for ML/AI workloads, collaborating with AI scientists and domain experts.

You will build scalable ELT/ETL processes, ensure data quality, and deploy containerized solutions using Airflow, Spark, and cloud services, contributing to Gen-AI workflows and knowledge-base development.

Qualifications

• Bachelor’s or Master’s degree in a related field.
• Strong Python and SQL skills; PySpark, Pandas, or Dask is a plus.
• Proven experience building data pipelines in cloud or hybrid environments.
• Experience with Gen-AI enablement including RAG pipelines and vector databases.
• Experience with API-based workflow automation and orchestration tools.
• Familiarity with SAP/ERP/MES data schemas and data governance.
• Hands-on experience with data lakes, warehousing, or streaming architectures.
• Excellent communication and cross-functional collaboration skills.

Responsibilities

• Build and Maintain Data Infrastructure: Design, implement, and maintain scalable ELT/ETL pipelines across diverse data sources (SAP, MES, WMS, ERP, IoT, etc.).
• Data Modeling & Analytics Support: Perform data wrangling for ML/AI model training and prepare datasets for time-series forecasting and Gen-AI apps.
• Collaborate Across Domains: Liaise with domain experts and developers to translate requirements into automated workflows.
• Maintain Data Quality & Governance: Implement checks, logging, and alerts; ensure data lineage and secure handling.
• Tooling and Deployment: Develop containerized, cloud-ready solutions; create APIs and automation components for scalable analytics deployment.

Skills

Python programming

SQL

Data modeling

Data governance

Cross-functional collaboration

Communication

Gen-AI knowledge

Education

Bachelor’s/Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field

Tools

Airflow

dbt

Kafka

Docker

Kubernetes

AWS / Azure

PySpark

Pandas / Dask

Job description

Key Responsibilities

• Build and Maintain Data Infrastructure
  Design, implement, and maintain scalable ELT/ETL pipelines across diverse data sources (SAP, MES, WMS, ERP, IoT, etc.)Develop automated data ingestion and transformation processes using modern tools (e.g., Airflow, dbt, Kafka, etc.)
• Data Modeling & Analytics SupportPerform data wrangling and preprocessing tailored for ML/AI model training and simulation environmentsWork with AI scientists to prepare datasets for time-series forecasting, optimization models, simulation environments, and Gen-AI applications such as retrieval-augmented generation, knowledge assistants, and automated reportingStructure and curate domain knowledge, metadata, and enterprise data assets to support reliable Gen-AI workflows, including prompt-ready datasets, semantic search, and knowledge-base development

• Collaborate Across DomainsLiaise with domain experts, supply chain analysts, and software developers to understand operational data needsServe as the bridge between raw data and AI solution pipelinesTranslate business and research requirements into automated workflows that connect data ingestion, analytics, model outputs, user interfaces, and operational decision processes
• Maintain Data Quality & GovernanceImplement checks, logging, and alerts to ensure high data reliability and traceabilityEnsure alignment with FAIR data principles and secure data handling practicesEstablish data lineage, validation, access control, and monitoring practices required for production-grade AI and Gen-AI solutions

• Tooling and DeploymentDevelop containerized and cloud-compatible data solutions (e.g., using Docker, Kubernetes, AWS, Azure)Contribute to end-to-end solution integration with dashboards, workflow automation platforms, digital twin systems, AI copilots, or decision-support applicationsDevelop reusable APIs, services, and automation components that enable scalable deployment of analytics, Gen-AI, and workflow solutions across research and industry projects

Job Requirements

• Bachelor’s/Master’sdegree in Computer Science, Data Engineering, Information Systems, or a related field
• Strong proficiency in Python and SQL; familiarity with PySpark, Pandas, or Dask is a plus
• Proven experience building data pipelines in cloud or hybrid environments
• Relevant hands‑on experience in Gen‑AI solution enablement, including RAG pipelines, vector databases, semantic search, knowledge‑base preparation, prompt engineering support, or LLM application integration
• Experience designing or implementing workflow automation solutions using APIs, orchestration tools, low‑code/no‑code platforms, robotic process automation, or enterprise automation frameworks
• Familiarity with supply chain data systems (SAP, ERP, MES) and industry‑specific data schemas is highly preferred
• Hands‑on experience with data lakes, data warehousing, or streaming architectures
• Excellent interpersonal and communication skills; ability to work in cross‑functional R&D teams
• Bonus: Familiarity with supply chain KPIs, AI/ML workflows, Gen‑AI application patterns, agentic workflows, and applied industrial analytics is advantageous

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.