Senior Data Analyst (Metadata)

Canberra, Australia

• Assigned Recruiter(s) Sri Ram Harsha Pillutla
• Initial Contract Duration 12 months
• Job Opening Status Open
• Extension Options up to a maximum 24 months
• Location ACT; QLD; VIC; SA; TAS
• Security Clearance Baseline
• Province Australian Capital Territory
• Country Australia
• Postal Code 2600

Working Arrangements

The Labour Hire Worker is expected to work onsite for the first 6 weeks before a hybrid flexible working arrangement can be considered, unless otherwise negotiated. A hybrid flexible working arrangement may then be considered, ensuring the Labour Hire Worker's residing address is within reasonable travel time and distance of the RFQ location to present onsite in cases of emergency or to meet business requirements. The location is the capital city of selected state/s including Cairns and Robina.

Essential criteria1.Data communication: Level 2 (DCF)Can develop and deliver effective narrative to communicate insights drawn from a range of data sources and outputs Can comfortably question data and results they are presented, and answer technical questions relating to their data, relevant to the audience Can communicate effectively between technical and non-technical experts across data production, management, or use, including clients, managers and data professionals Can effectively communicate the relationship between the data and the context in which it is used2.Data outputs, products or services: Level 2 (DCF)Is responsible for the production of a data output, product or service. Can create data outputs or products and deliver data services in accordance with established processes and systems, and can explain decisions made at all stages3.Identify research questions: Level 2 (DCF)Can identify and structure relevant research questions for specific needs, and develop the approach and specific measures to resolve those questions4.Integrate data: Level 1 (DCF)Has a basic understanding of how data can be linked with other data Understands what data integration is, including its benefits and limitations Understands what types of data can be combined for analysis5.Statistical data analysis: Level 2 (DCF)Can use specialist statistical applications for statistical models Is able to understand and amend existing code (programming language) to conduct analytical tasks6.Subject matter expertise: Level 2 (DCF)Has a comprehensive knowledge of the subject matter area associated with the data use and can readily identify the parameters of the subject matter that influence the use of the data7.Visualise data: Level 1 (DCF)Can interpret basic data visualisations like standard charts and explain them to others Can create basic visualisations such as tables and graphs

Job Description Senior Metadata Officer

Data Analysts the design, analysis and delivery of relevant, trusted and objective statistics and data-related insights.

As the Data Manager you will support the uplift of business metadata by leading metadata development for assigned datasets, supervising metadata developers, driving engagement with business and technical stakeholders, and ensuring that developed metadata is consistent, complete and fit for registration.

Primary focus

Operational leadership, stakeholder engagement, hands-on metadata development and quality assurance.

• Expertise implementing the ISO/IEC 11179 standard formetadata registries.
• Knowledge of other relevant standards, such as ONDCmetadata attributes, National Archives of Australia requirements and DCAT, or demonstrated ability toacquire this knowledge.
• Demonstrated experience leading metadata development, quality assurance or related data management work.

Highly desirable skills and knowledge

• Data governance and data management experience.
• Strong research, analysis and synthesis skills.
• Strong written and verbal communication and facilitationskills.
• Advanced Microsoft Excel and project coordination skills.
• Experience supervising or coaching staff.
• Ability to quickly acquire knowledge of agency programsand the agency data landscape.

Ability to

• think strategically and align operational activityaccordingly
• lead and motivate staff to achieve quality outcomes
• manage, mentor and guide staff learning anddevelopment
• develop and manage a range of internal and externalstakeholder relationships
• assess risks and implement mitigation strategies
• manage and resolve conflict using appropriate strategies
• grasp expectations and concerns, and negotiate qualityoutcomes
• understand the audience and context, and effectivelytailor communication and communication styles
• develop long term plans for the team and manage workin the context of business priorities
• undertake objective, systematic analysis and drawaccurate conclusions based on evidence
• make decisions based on professional judgement, riskevaluation and in the context of the environment
• work highly independently under broad direction and act collaboratively
• manage staff and competing priorities to achieve qualityoutcomes and project completion
• lead change initiatives and support staff throughuncertainty and the change process.

Key duties and responsibilities Metadata workstream leadership

• Contribute to project scope, governance arrangements, success measures, delivery cadence and reporting requirements.
• Support prioritisation of high-value data assets across approved AI projects and relevant agency program data.
• Plan and coordinate metadata development for assigned datasets and allocate work to metadata developers.
• Track progress, quality, blockers, risks, coverage and registration status using the project tracker or dashboard.
• Support transition of registered records to ongoing stewardship and maintenance arrangements.

Metadata analysis, development and assurance

• Analyse and curate existing metadata records, Enterprise Data Warehouse artefacts, data dictionaries, data models, lineage information, source-system documentation and project materials.
• Assess metadata coverage, gaps, duplication, quality issues and terminology inconsistencies across related assets.
• Harmonise definitions, naming, business context and governance attributes where appropriate.
• Locate, interpret and synthesise technical documentation, business rules, lineage artefacts, legislation, policies, procedures and stakeholder advice.
• Develop metadata using clear, consistent, plain-English definitions and data asset descriptions for business and technical audiences.
• Review metadata produced by the workstream to assure consistency, completeness and quality.

Stakeholder engagement and staff supervision

• Lead engagement with subject matter experts, data stewards, data engineers and analysts, and AI project contacts.
• Validate metadata with relevant business and technical stakeholders.
• Submit metadata for registration and manage feedback through to agreed status.
• Deliver capability uplift in ISO/IEC 11179 and agency metadata standards.
• Completed metadata work packages for assigned datasets.
• Validated and harmonised metadata definitions and supporting artefacts.
• Quality reviews and documented resolution of issues and feedback.
• Current progress, coverage, risk and registration tracking.
• Stakeholder engagement and validation records.
• Coaching and guidance for other metadata developers.
• Inputs to stewardship and maintenance handover arrangements.