Associate Architect

Job Title: Associate Architect

University-Level Unit: NUS Information Technology

Faculty/Department-Level Unit: Applications - Applications Architecture & Technology

Employee Category: Executive & Administrative

Location_ONB: Kent Ridge Campus

Posting Start Date: 12/05/2026

About NUS IT

NUS Information Technology is the cornerstone to providing reliable, high-performance and secure IT solutions and effective IT governance for the campus. Here at NUS IT, we aim to transform NUS into a borderless computing community providing knowledge at its fingertips by enhancing the use of effective applications and services for teaching and learning.

We drive a culture that is forward-looking. With a strong passion for IT, our people are always striving to improve, push boundaries and innovate with a "can-do" attitude. We embrace collaboration, open communication and knowledge sharing. If you see yourself thriving in a dynamic environment and breaking new grounds with innovative ideas, you will find yourself at home in NUS IT.

As part of our team, you can look forward to an empowered work environment that allows you to take charge of your own career path. We provide competitive remuneration as well as flexible work arrangements to enable your growth and development. We pride ourselves on our diverse workforce and are committed to transforming NUS into a leading global University shaping the future.

Job Description

We are looking for a Data Engineering Lead to design, build, and scale robust data platforms and pipelines. You will lead and guide a team of data engineers to deliver reliable, secure, and high-quality data systems that support analytics, AI, and application consumption.

Duties and Responsibilities

Team Leadership

Lead, mentor, and grow a team of data engineers

Drive agile delivery, conduct code reviews, and promote engineering best practices

Collaborate with data product teams and stakeholders to prioritize initiatives

Technical Leadership

Design end-to-end data engineering solutions

Provide technical leadership to solve complex engineering challenges

Ensure strong data quality, reliability, and observability across pipelines

Stay current with emerging technologies to drive continuous improvement and innovation

Lead the adoption of AI-assisted engineering practices across the development lifecycle

Drive the use of AI tools (e.g., copilots, agents) to enhance engineering productivity and quality

Data Engineering

Build and maintain data pipelines and consumption layers (e.g., APIs, databases)

Develop and manage data lakehouses and data warehouses

Implement streaming and real-time data solutions where required

Enable data readiness for model training, feature engineering, and inference workflows

Apply AI-driven data engineering practices in day-to-day development

Use AI to generate data engineering artifacts and automate workflows from requirements to production-ready outputs

Quality Assurance & Governance

Establish testing and quality control practices to ensure reliable data delivery

Ensure alignment with university regulatory and compliance requirements

Qualifications

Must Have

Previous technical lead experience

Strong experience with cloud data platforms

Proficiency in Python with strong SQL skills

Experience with distributed data processing (e.g., Spark)

Hands-on experience with data orchestration tools

Hands-on experience with data quality practices (testing, validation, and monitoring)

Experience with AI-driven data engineering lifecycle practices

Experience using AI tools (e.g., copilots, agents) to enhance engineering productivity and quality

Experience supporting data pipelines for analytics and/or AI/ML use cases

Solid understanding of data modelling and data warehousing concepts

Proven ability in system design, architecture, and leading engineering teams

Strong problem-solving and critical thinking skills

Strong verbal, written, and stakeholder communication skills

Nice to Have

12+ years of experience in data engineering or related data roles

Experience with enterprise and open-source data platforms (e.g., Microsoft Fabric, Databricks, Informatica, Apache Spark, Trino, ClickHouse, DuckDB)

Experience with CI/CD practices for data pipelines

Exposure to real-time/streaming and event-driven architectures