Full-Stack Data Engineer

• School of Electrical Engineering and Computer Science

• Full-time (100%), fixed-term position for up to 3 years

• Base salary will be in the range $91,658.60 - $98,330.37 + 17% Superannuation (HEW Level 6)

• Based at our St Lucia Campus

About This Opportunity 

We are seeking a strong candidate who will support the design and implementation of software, configuration scripts, and data processing workflows for ongoing research projects. Additionally, this role will involve data engineering tasks such as acquiring, processing, and managing health-related datasets, from research databases like MIMIC to real-world electronic medical records from platforms such as Cerner and Epic. Beyond data engineering, the role includes aspects of data science, such as conducting data analysis, feature engineering, and building machine learning models for tasks like classification and regression

Key responsibilities will include: 

• Collaborate with Researchers and Students: Work closely with researchers and higher-degree research students to understand the data and software requirements of ongoing projects. Provide support for software design and implementation, data acquisition, exploration, wrangling, and pre-processing tasks. Also, prepare cloud or on-premise infrastructure for related projects.

• Full-Stack Development: Design, develop, and maintain both the front-end and back-end components of data-centric applications, including Natural Language Processing (NLP) and Information Retrieval (IR) systems, Retrieval-Augmented Generation (RAG), and AI/ML-driven applications.

• Data Pipeline Management: Build and manage robust data pipelines using open-source or cloud tools, such as Apache Airflow, AWS Managed Workflows, Google Cloud Composer, that ensure the efficient flow of data through the system, from ingestion and transformation to storage and access, supporting the needs of various applications.

• User Interfaces and API Development: Implement intuitive user interfaces and robust APIs that interact with underlying data systems, enabling seamless access and manipulation of data by end users and applications.

• Documentation and Knowledge Sharing: Create and maintain technical documentation for data systems, pipelines, and applications. Provide training and support to researchers and students on best practices for utilising the developed tools, systems and infrastructures.

• Stay Current with Industry Trends: Keep up-to-date with the latest trends and advancements in data engineering, software development, cloud technologies, and data security, applying new knowledge to improve the group’s capabilities.

• Data Security and Integrity: Ensure that all data processing, storage, and access layers are secure and compliant with relevant data governance and privacy regulations. Monitor and optimise the performance, reliability, and security of data systems.

About UQ

As part of the UQ community, you will have the opportunity to work alongside the brightest minds from all over the world who have joined us.

Everyone here has a role to play. As a member of our professional staff cohort, you will be actively involved in working towards our vision of a better world. By supporting academic endeavours across teaching, research, and student life, you will have the opportunity to contribute to activities that have a lasting impact on our community.

Join a community where excellence is at the core of our culture, contributions are valued, and a range of benefits and rewards are available, such as:

• Up to 26 weeks paid parental leave or 14 weeks paid primary caregiver leave

• 17% superannuation contributions

• 17.5% annual leave loading

• Access to flexible working arrangements including hybrid working options, flexible start/finish times, purchased leave, and a condensed fortnight

• Health and wellness discounts – fitness passport access, free yearly flu vaccinations, discounted health insurance, and access to our Employee Assistance Program for staff and their immediate family

• UQ Study for Staff

• On-campus childcare options

• Affordable parking (from just $6.15 a day)

• Salary packaging options

About You

Our ideal candidate will be a self-motivated, solutions focused Full-Stack Data Engineer with excellent communication skills.

• Qualifications and training equivalent to an undergraduate degree in Computer Science, Data Engineering, Data Science, Health Informatics, or related field and significant relevant experience; or an equivalent combination of relevant experience and/or education/training.

• 1-2 years of proven experience as a Data Engineer or Data Scientist, focusing on working with healthcare datasets, such as MIMIC, EHRs, or other health information systems.
  Proficiency in SQL, Python, or other programming languages used for data manipulation and ETL processes.

• Experience with cloud platforms, such as AWS or GCP, for data storage and processing.
  Ability to collaborate effectively with interdisciplinary research teams, including non-technical stakeholders.

• Strong problem-solving skills and the ability to work independently in a fast-paced research environment.

• Detailed knowledge in the following areas, with significant experience in:

  • Large Language Model Frameworks: Familiarity with the tools or frameworks supporting LLM development in applications (e.g., LangChain, Ollama, LlamaIndex), with a solid understanding of how to integrate and utilize these frameworks effectively in real-world solutions.

  • Containerisation and Orchestration: Experience with containerisation (e.g., Docker) and orchestration tools (e.g., Kubernetes) for deploying and managing applications at scale, including support for GPU-accelerated applications.

  • Data Engineering Tools: Proficiency in data engineering tools, including Apache Airflow for workflow orchestration, and message brokers like RabbitMQ or Kafka for handling real-time data streams.

  • Cloud Infrastructure and DevOps: Ability to work with cloud platforms (e.g., AWS, GCP) and implement DevOps practices such as CI/CD pipelines, Infrastructure as Code (e.g., Ansible, Terraform), and continuous monitoring (e.g., Prometheus, Grafana).

  • Database Technologies: Strong understanding of database systems, including SQL (e.g., PostgreSQL), NoSQL (e.g., MongoDB, Redis), and GraphDB (e.g., Neo4j, TigerGraph), with solid data modelling and query optimisation skills.

  • Full-Stack Application Development: Experience in building and deploying full-stack applications that integrate data pipelines, APIs, and data-driven features.

  • Machine Learning: Proficiency in using and coding with machine learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch) and experience with cloud-based ML services (e.g., AWS SageMaker, Google AI Platform).

  • (Optional) Back-End to Front-End Development: Additional experience in full-stack development, including proficiency in JavaScript, Node.js, and Python for building robust and scalable web applications.

The successful candidate may be required to complete a number of pre-employment checks, including: right to work in Australia and criminal check. 

You must maintain unrestricted work rights in Australia for the duration of this appointment to apply. Employer sponsored work rights are not available for this appointment.

Questions? 

For more information about this opportunity, please contact Dr Teerapong Leelanupab t.leelanupab@uq.edu.au. For application inquiries, please reach out to the Talent Acquisition team at talent@uq.edu.au, stating the job reference number (below) in the subject line. 
 

Want to Apply? 

All applicants must upload the following documents for your application to be considered:

• Cover letter summarising how your background aligns to the ‘About You’ section  

• Resume 

Other Information 

UQ is committed to a fair, equitable and inclusive selection process, which recognises that some applicants may face additional barriers and challenges that have impacted and/or continue to impact their career trajectory. Candidates who don’t meet all criteria are encouraged to apply and demonstrate their potential. The selection panel considers both potential and performance relative to opportunities when assessing suitability for the role.

We know one of our strengths as an institution lies in our diverse colleagues. We're dedicated to equity, diversity, and inclusion, fostering an environment that mirrors our wider community. We're committed to attracting, retaining, and promoting diverse talent. Reach out to talent@uq.edu.au for accessibility support or adjustments.

Applications close Monday 26 May 2025 at 11.00pm AEST (R-49020). Please note that interviews have been tentatively scheduled for Friday 30 May 2025 from 2.30pm AEST.

#LI-DNI