Principal Data Modeller

3 weeks ago


Brisbane, Australia Queensland Treasury Full time

The Analytics Division is comprised of two core branches, Business Intelligence, and the Data Centre of Excellence, which together, are responsible for supporting the integrity of the revenue system by providing advanced data analytics and business intelligence services across QRO and external client groups.
As the Principal Data Modeller, your responsibilities include, but are not limited to:

- Undertaking complex analysis and development activities in a range of technologies

**To be successful in the role you will need to demonstrate**:

- Strong leadership and communication skills
- Understanding and working knowledge of SQL, agile process, data modelling, business modelling and data flow development in Data Warehouse utilising different data sources.
- Knowledge of CI/CD development practices such as Git, Jenkins.
- Experience with the tools such as Azure Data Lake Storage, Azure Synapse, Data Bricks, SAP HANA 2.0 XSA.

We support a thriving and future-focused workforce which offers flexible working practices (e.g., working from home) to ensure suitable and tailored work/life balance whilst meeting organisational, community and government needs. The position is based in our Brisbane office with high quality end of trip facilities available and access to professional growth pathways such as the Study and Research Assistance Scheme (SARAS).
If you are interested in this role - click 'Apply' to be redirected to the full advertisement, including role description and contact details.
Applications may remain current for 12 months after the closing date of the vacancy and may be used to fill identical or similar roles.
We welcome applicants to share with the panel chair any health condition, disability and/or cultural/linguistic background, so that we may make any necessary adjustments to enable you to equitably participate in the recruitment process.

**Job Ad Reference**: QLD/562598/24
**Closing Date**: Tuesday, 21 May 2024This work is licensed under a Creative Commons Attribution 3.0 Australia License.



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