Job description

[What the role is]

[What you will be working on]

Your responsibilities will include, but are not limited to, the following:

• Lead in design, development and review curriculum, facilitate student learning, supervise projects, conduct assessments

• Participate in and contributes to professional development activities such as industrial research and development projects or publication/presentation of papers

• Work with enterprises to develop partnership opportunities on industry projects, curriculum development, and/or training of learners

• Drive cross-functional/project teams focused on developing practical solutions for real-world challenges, with emphasis on translating research into applicable outcomes and providing consultancy for practical implementation

• Perform associated tasks for modules related to Data Engineering, Data architecture, Analytics, Cloud technologies and other ICT-related modules

• Support student development in areas of Education and Career Guidance

• Solicit internship opportunities with industry partners and supervise student internships

• Participate in ad hoc projects pertaining to academic enhancement, student development, publicity and outreach and Continuing Education and Training (CET)

[What we are looking for]

• At least 3 years of relevant work experience in designing, implementing, or supporting data virtualisation solution, big data platforms and pipelines, and/or data lake and data warehouse systems for large‑scale data environments.

• Proficient in Analytical Programming using Python and/or other related programming languages.

• Ability to clearly explain data architecture and data engineering concepts in an applied teaching context.

Additional pre-requisites that are advantageous

• Experience in platforms such as Snowflakes, Denodo, Apache Spark, Hadoop ecosystem, Alteryx or equivalent.

• Experience in designing and building systems for collecting and analysing data (structured and unstructured) at scale with data integration, ETL/ELT processes, data modelling, and performance optimisation.

• Practical exposure to data virtualisation platforms and/or concepts (e.g. virtual data layers, federated queries, abstraction over heterogeneous sources).

• Knowledge of Cloud/Data Engineering DevOps tools such as Azure/AWS Cloud, Apache Spark, Apache Kafka, PostgreSQL, Airbyte and modern data repositories will be an advantage.