Senior Data Analyst

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. Essential requirements • Expertise implementing the ISO/IEC 11179 standard for metadata registries. • Knowledge of other relevant standards, such as ONDC metadata attributes, National Archives of Australia            requirements and DCAT, or demonstrated ability to acquire 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 facilitation skills. • Advanced Microsoft Excel and project coordination skills. • Experience supervising or coaching staff. • Ability to quickly acquire knowledge of agency programs and the agency data landscape. Ability to: • think strategically and align operational activity accordingly • lead and motivate staff to achieve quality outcomes • manage, mentor and guide staff learning and development • develop and manage a range of internal and external stakeholder relationships • assess risks and implement mitigation strategies • manage and resolve conflict using appropriate strategies • grasp expectations and concerns, and negotiate quality outcomes • understand the audience and context, and effectively tailor communication and communication styles • develop long term plans for the team and manage work in the context of business priorities • undertake objective, systematic analysis and draw accurate conclusions based on evidence • make decisions based on professional judgement, risk evaluation and in the context of the environment • work highly independently under broad direction and act collaboratively • manage staff and competing priorities to achieve quality outcomes and project completion • lead change initiatives and support staff through uncertainty and the change process. Key duties and responsibilities Duties may include some or all of the following: 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. Expected deliverables • 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. Criteria The buyer has specified that each candidate must provide a one page pitch to address all criteria specified. This is equal to 5000 characters. Essential criteria 1. 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 used 2. 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 stages 3. 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 questions 4. 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 analysis 5. 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 tasks 6. 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 data 7. 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 Opportunity summary Sellers can submit Up to 3 candidates Number of sellers invited Between 5 and 10 Number of candidates submitted Fewer than 10 RFQ closing time 02 Weeks 01 Days 06 Hours Send us feedback About Accessibility Privacy Terms of use Disclaimer and copyright An initiative of the Digital Transformation Agency The Australian Government acknowledges the Traditional Owners of Country throughout Australia and acknowledges their continuing connection to land, waters and community. We pay our respects to the people, the cultures and the Elders past and present. © Commonwealth of Australia