About NUS IT

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