Lead Data Analyst

Role/s: 1 x EL1 Data Analyst The EL1 Data Analyst (Labour Hire) provides specialist data analysis, data discovery and data delivery support for complex ICT programs, projects and digital services. Working in multidisciplinary delivery teams, the role examines complex data from internal and external sources, defines how data should be interpreted and used, and translates business needs into clear data requirements, mappings, rules and analytical outputs. The role works closely with business owners, data engineers, business analysts, architects, developers, testers and technical specialists across the end-to-end data lifecycle, from source discovery and profiling through to ingestion, validation, reconciliation, reporting and production support. Supplier Briefing: A supplier briefing will not be held for this role. Rate: The proposed rate should reflect candidate's skills and experience against the Evaluation Criteria. Candidates that do not demonstrate these criteria will not be assessed. Candidates should be aware that rates may be negotiated further as part of the selection process. Citizenship: As part of the eligibility and suitability requirement, NDIA seeks Labour Hire Workers who are Australian citizens only. Successful candidates will be required to furnish valid evidence of citizenship during the Pre-engagement Check. Labour Hire Licence: Applicable for ACT, VIC and QLD: Labour hire licences are required in the state that specified personnel are being contracted. Key duties and responsibilities Lead data discovery and profiling across complex internal and external datasets to understand their structure, content, quality, lineage, relationships, constraints, sensitivity and fitness for purpose. Work with business and technical stakeholders to identify required data, clarify its business meaning, define the intended use and translate business outcomes into detailed data requirements and agreed data contracts. Develop and maintain source-to-target mappings, data dictionaries, field definitions, data contracts, transformation rules, join logic, filtering rules, matching logic and other implementation-ready data specifications. Analyse data using SQL and relevant analytics tools to identify duplicates, outliers, nulls, invalid values, inconsistent formats, referential issues and other data quality concerns. Define practical options for cleansing, standardising, transforming, matching, de-duplicating and reconciling data, including exception handling, rejected records, historical data, initial loads, incremental loads and refresh processes. Collaborate with data engineers and developers to translate approved mappings, data contracts and business rules into implementable ingestion, integration, migration and transformation logic, while maintaining clear end-to-end traceability. Support the design and delivery of data pipelines, bulk ingestion processes, system integrations, migrations and data refreshes across cloud and on-premises environments by clarifying requirements, resolving data questions and reviewing technical outcomes. Develop or prepare reusable datasets, SQL queries, analytical code, scripts, notebooks and supporting artefacts that enable profiling, transformation, reconciliation, testing and controlled implementation. Validate source and target data against approved mappings and data contracts, reconcile counts and key measures, investigate failed or incomplete loads, and support resolution of defects, exceptions and production data issues. Work with testers and business representatives to develop test scenarios, expected results and traceability from approved data requirements and mapping rules. Produce clear analysis, dashboards, reports, visualisations and written advice for both technical and non-technical audiences where required by the program or project. Identify and communicate data risks, assumptions, dependencies, limitations and gaps early, and provide evidence-based options and recommendations to delivery teams and senior stakeholders. Apply relevant data governance, privacy, security, records management and information handling requirements throughout analytical and delivery activities. Contribute to continuous improvement of data standards, documentation, reusable methods, quality controls and ways of working across data and digital delivery teams.   (NOTE: the key responsibilities of the role are based on current priorities and may change over time) Criteria The buyer has specified that each candidate must provide a response to each criterion. Each response is limited to 3000 characters. Essential criteria 1. Demonstrated extensive experience undertaking data discovery, profiling and analysis across complex datasets in an ICT, digital transformation or data delivery environment. 2. Strong practical experience using SQL and contemporary data analysis tools to interrogate complex data, develop analytical code or scripts, identify quality issues and undertake detailed technical investigation. 3. Demonstrated experience developing source-to-target mappings, data contracts, data definitions, transformation and business rules, join and matching logic, exception handling and other implementation-ready data specifications. 4. Demonstrated experience working across the end-to-end data lifecycle, including discovery, lineage and traceability, ingestion or integration, migration, initial and incremental loads, validation, reconciliation, defect investigation and implementation support. 5. Demonstrated ability to translate complex business and technical requirements into clear data artefacts and delivery specifications, and communicate effectively with business stakeholders, engineers, architects, developers and testers. 6. Strong analytical, problem-solving, documentation and stakeholder engagement skills, with the ability to work independently at EL1 level and collaboratively in multidisciplinary delivery teams. Desirable criteria 1. Experience using SAS Viya or SAS Studio, Snowflake, SQL, Python, Power BI, Talend, Azure data services or comparable data and analytics technologies. 2. Experience supporting large-scale data migration, bulk ingestion, cloud data platforms, system integration, data refresh or digital transformation initiatives in a complex environment. 3. Experience with data modelling, entity and relationship data, reference data, de-duplication, matching, lineage or reconciliation approaches. 4. Knowledge of Australian Government data governance, privacy, security and information management requirements. Opportunity summary Sellers can submit Up to 2 candidates Number of sellers invited More than 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