Lead Data Manager

Metadata Project Lead Data Managers support organisations with the sourcing, acquisition, management, integration and sharing of data to provide business insights and drive data-based decision making. As the Data Manager you will be accountable for the end-toend uplift of business metadata for priority high-value data assets. The role plans and governs the body of work, sets priorities, leads metadata registrars and developers, and ensures delivery is aligned with strategic objectives, AI readiness outcomes and applicable metadata standards. Primary focus Project leadership, governance, delivery assurance, team leadership, strategic stakeholder engagement and quality oversight. Knowledge and Qualifications: • Demonstrated expertise implementing the ISO/IEC 11179 standard for metadata registries.. • Knowledge of relevant metadata and information management standards, including ONDC metadata attributes,      National Archives of Australia requirements and Data Catalogue Vocabulary (DCAT). • Demonstrated experience leading complex data, metadata or governance projects and multidisciplinary teams. • Data governance and data management expertise. • Strong research, synthesis and analytical capability. • High-level written and verbal communication and stakeholder-management skills. • Advanced Microsoft Excel and project tracking skills. • Ability to quickly acquire knowledge of agency programs and the agency data landscape. • Experience supporting analytics, automation or AIrelated data initiatives. 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: Project governance and delivery leadership • Develop and maintain the project scope, governance arrangements, success measures, delivery cadence    and reporting requirements. • Confirm and prioritise high-value data assets across approved AI projects and relevant program areas. • Set work priorities, allocate resources and maintain accountability for delivery milestones and outcomes. • Track progress, quality, blockers, risks, coverage and registration status through fit-for-purpose project    trackers or dashboards. • Ensure delivery remains aligned with agency strategic objectives and AI readiness outcomes. Metadata leadership and quality assurance • Lead analysis and curation of the current metadata landscape, including existing metadata records,   Enterprise Data Warehouse artefacts, data dictionaries, lineage information, source-system documentation and      project materials. • Oversee assessment of metadata coverage, gaps, duplication, quality issues and terminology inconsistencies         across related data assets. • Direct harmonisation of definitions, naming, business context and governance attributes where appropriate. • Ensure new metadata uses clear, consistent, plain-English definitions and descriptions suitable for business and    technical audiences. • Review and assure the consistency, completeness and quality of metadata before or during registration. Stakeholder and team leadership • Identify and engage key subject matter experts, data stewards, data engineers and analysts, and AI project   contacts. • Lead and manage metadata registrars and metadata developers, including performance, attendance, workload,       health and wellbeing. • Deliver training and capability uplift in ISO/IEC 11179 and agency metadata standards. • Oversee stakeholder validation, metadata registration feedback and resolution through to agreed status. • Transition registered records to sustainable stewardship and maintenance arrangements. • Lead and support operations of the team including monitoring business performance, setting priorities, and            managing resources and workflow. • Lead and manage staff including overseeing the management of performance, attendance and health and              wellbeing. • Develop staff capability through coaching, mentoring and succession planning to increase performance,   engagement and retention. • Undertake and assist with business management and workforce planning. • prepare and review a range of written material including technical and corporate documentation • Report on the achievement of business outcomes, key performance indicators and quality assurance to manage    overall performance. • Assist with the development of strategies, operational policy and procedures and change initiatives. • Act with integrity by promoting workplace practices aligned with the APS Values and the APS Code of Conduct. • Exercise delegation in accordance with legislation and guidelines. 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 access: Level 3 (DCF) Can mitigate issues arising from different access approaches Can make and justify recommendations for data access Can provide actionable strategic advice on data access 2. Data and information management: Level 3 (DCF) Is an expert resource for implementing and shaping the organisation's strategic use of data and information management good practice, and can advise others. This includes requirements relating to data access, data security, privacy and ethics Represents a point of contact for data and information management leads in other organisations 3. Data classification: Level 3 (DCF) Is an expert resource on data classifications and coding protocols and advises others Can employ conceptual frameworks in support of data classification and coding 4. Data collection: Level 3 (DCF) Is an expert resource in all aspects of data collection, including why data is collected and the roles associated with collection Can make justifiable decisions about how data is collected Can write custom scripts and code (programming language) to collect data 5. Data collection methodology: Level 3 (DCF) Is an expert resource on data collection methodology Can make justifiable recommendations to address data collection issues Maintains understanding of new trends and innovations relating to data collection methodology (including Artificial Intelligence-based technologies) and develops own capabilities in these where relevant 6. Data editing: Level 3 (DCF) Is an expert resource on different methods of data editing and advises others Can assess current editing methods critically Can build capability of others in data editing concepts and methods 7. Data governance: Level 3 (DCF) Is an expert resource in data governance and can formulate and advise on data governance policies and contribute to the structure of organisational data governance frameworks (includes data access, data security, privacy and ethics) Represents a point of contact for data governance leads in other organisations Maintains awareness of new legislative requirements and trends that impact good data governance and ensures organisational compliance with these (includes data access, data security, privacy and ethics) Can provide data governance thought leadership across broader data use contexts 8. Data integrity and quality assurance: Level 3 (DCF) Is an expert resource in the use of measures for data quality assurance, the interaction of those measures, and their application in conjunction with one another Can advise others on the use of data quality measures to make accurate assessments of data fitness-for purpose (including trustworthiness and accuracy) Is an expert resource on ensuring data integrity across its lifecycle, and advises others 9. Data outputs, products or services: Level 3 (DCF) Has expert knowledge about the production of a data output, product or service Provides expertise in developing new products or data services to meet evolving requirements, needs and opportunities. Can build the capability of others in the delivery of data outputs, products or services 10. Data use and re-use: Level 2 (DCF) Understands and can articulate the value of data in terms of use and re-use Implements a variety of techniques to ensure data is open and can be used beyond the specific purpose for which it was collected, in accordance with legislative requirements and organisational guidelines (includes data sharing legislation) 11. 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 12. Integrate data: Level 3 (DCF) Can perform and provide expert advice on data integration Can build capability of others in understanding and applying good data integration practice and principles Can write custom scripts and code (programming language) to integrate data Maintains understanding of new trends and innovations relating to integrating data (including Artificial Intelligence-based technologies) and develops skills in these where relevant 13. Metadata - Describe and summarise data : Level 2 (DCF) Can use various summary options to effectively describe data, and explain and justify those choices Follows organisational standards and procedures relating to metadata creation, storage and use. Can access metadata and use the descriptors to better understand existing data and effectively use it. 14. Sourcing and use of administrative data: Level 3 (DCF) Is knowledgeable about multiple sources of administrative data Can advise on how the data has been used to produce new insights Provides expertise to identify new sources of administrative data as well as uses for that data Can write custom scripts and code (programming language) to source and combine administrative data Maintains understanding of new trends and innovations relating to sourcing of administrative data(including Artificial Intelligence-based technologies) and develops capabilities in these where relevant 15. Value organisational data as assets: Level 2 (DCF) Has extensive knowledge of the organisation's data assets, including a comprehensive understanding of how their fitness for purpose translates to value for the organisation Promotes opportunities for using data to support decision making, advice and research Considers the broader environment and context when drawing or interpreting insights from data. Uses these insights to make informed, evidence-based decisions and recommendations, and act accordingly 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 00 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