Data Analyst

Description:

Application closing date: Wednesday, 10 April 2024 • 11:59pm, Canberra time

Estimated start date: Wednesday, 01 May 2024

Location of work: NSW

Working arrangements: Flexible work arrangements available

Length of contract: 12 months

Contract extensions: 2 x 12 months

Security clearance: Must be able to obtain Baseline

Rates: $80 - $110 per hour (inc. super)

The Biosecurity Analytics Centre, Digital Business Division, requires skilled data analysts to work in a high performing data warehousing team delivering analytics products to support business outcomes. The team member will provide services under direction including:

1. Define and formulate data-related problems and objectives in collaboration with domain experts and business stakeholders.
2. Analyse data using descriptive, inferential, and predictive methods to answer business questions and test hypotheses.
3. Visualize and present data using charts, graphs, tables, and other formats to convey key insights and trends.
4. Support data engineers who will provide data curation services.
5. Design and maintain reference data tables according to the team’s reference data standards using manual and automated mechanisms.
6. Create and maintain reports, dashboards, and documentation to support data analysis and interpretation.
7. Identify and implement opportunities to improve data quality, efficiency, and scalability.
8. Write clear, business meaningful, code documentation that explains the intent and mechanics of scripts.
   

The data analyst is a versatile role employing a mixture of data engineer and data scientist skillsets. The interested analyst can specialise in data engineering or data science.

The staff will be part of a team that is:

1. Business-focused – highest priority is given to considering the business problem and ensuring the technical work is solving real-world problems.
2. Code-first – highest technical rigor in code is demanded, expectations are clearly defined and to be met.
3. Multi-disciplinary – team members are to be considerate of how staff from other professions contribute to the overall outcome, and collaborate accordingly.
4. Open – code, communication, documentations, peer-review are open-by-default, including early drafts of work.

Essential Criteria

1. Data analysis - ability to derive business intelligence through SQL, Power BI or Python exploration of curated business data, and communicate these persuasively through Power BI reports. The analyst aspires to be the multidisciplinary team's forerunner to delivering value by formulating and answering business questions as realistic data problems. They do this by proactively understanding the business process, the lineage and quality of the data capture, and the strategic intent of the business. Ability to extract and query data using SQL is essential. (Response Weighting 34%)
2. Business analysis – business analysis of complex digital systems through stakeholder engagement, SQL data analysis and researching available resources. The goal of the analysis is to capture the intent of business processes and the flow of information through these processes. The analyst aspires to be the subject matter expert for the multi-disciplinary team. They communicate findings effectively to technical and non-technical audiences. Ability to verify hypotheses using SQL data analysis is essential. (Response Weighting 33%)
3. Team player – ability to work independently with limited guidance to create self-direction in uncertain and rapidly changing environments, displaying the characteristics of creativity and resilience. Ability to work openly and collegiately in a multi-disciplinary team, displaying a genuine consideration of how members from other disciplines contribute to the overall outcome. They have a strong focus on people and business outcomes over formal methodologies. (Response Weighting 33%)

Desirable Criteria

1. Data engineering - ability to create high business value, robust and efficient SQL pipelines to curate data from complex and high-volume sources. The goal of data engineering is to create useful and trustworthy data representations of real-world business processes. The engineer aspires to be the pillar of the multidisciplinary team by providing a constant access to the data needed to solve the next problem. They appropriately question requirements and take responsibility for checking outputs against independent testing. Ability to tackle complex SQL problems is paramount.
   
2. Reference data - ability to integrate disparate data assets through judicious use of reference data. By using a common standard as a translation of data from disparate systems, decision makers or analysts obtain a seamless view of multiple systems. The data engineer achieves this by designing golden reference tables, maintaining mapping tables between individual systems to the common reference, and create automated monitoring for outdated references.
3. Familiarity with the Australia’s biosecurity systems. Familiarity with the imported cargo pathway will be highly regarded.
4. Familiarity with the Australia’s exports systems.