Position: Data Analyst

Location: Sydney

Duration: 6 months

Position Summary

We don’t sit back and wait for the future to happen—we craft it through new technology, innovation, and investment. As a challenger brand with a challenger spirit, seeking a Data Analyst (around 6 years’ experience) to support our enterprise‑wide data lifecycle management and data retention initiatives while delivering high‑quality insights for business stakeholders.

You will work closely with the Data Product Manager, solution architects, application owners, and engineering teams to analyze data structures and application landscapes, design meaningful reports and visualizations, translate requirements into clear backlog items, and help drive compliant, scalable data management patterns across the Optus group.

Key Responsibilities

Data Analysis & Insights

• Perform data extraction, profiling, cleansing, and transformation using SQL and Power BI/Excel to produce accurate, trusted datasets.
• Build and maintain dashboards, reports, and ad‑hoc analyses that provide actionable insights for business and technology stakeholders.
• Present findings clearly—telling the story with data, trends, anomalies, and recommended actions.

Data Lifecycle & Risk Support

• Investigate applications and their data to assess retention needs, risks, and compliance implications across brands, segments, and channels.
• Map data flows and integration points between systems; document data lineage, usage, and control gaps with SMEs and application owners.
• Partner with solution architects and product managers to evaluate retention and data management solutions; identify opportunities for automation and process improvement.

Requirements, Documentation & Stakeholder Collaboration

• Lead/participate in requirement‑gathering sessions and workshops; translate outcomes into Epics, User Stories, Acceptance Criteria, and Test Cases in Jira.
• Maintain clear documentation (source‑to‑target mappings, calculation logic, validation rules, report specs) to ensure transparency and repeatability.
• Engage effectively with cross‑functional teams (engineering, security, risk, legal, business) and manage expectations through structured updates.

Delivery & Agile Ways of Working

• Contribute to backlog refinement and prioritization, ensuring items are well‑scoped, testable, and aligned to timelines and value outcomes.
• Support and/or facilitate agile ceremonies (stand‑ups, sprint planning, reviews/demos, retrospectives); drive continuous improvement in quality and throughput.
• Validate deliverables against the Definition of Done and acceptance criteria; drive rigorous data quality checks and reconciliation.

Quality, Governance & Compliance

• Apply data quality frameworks (completeness, accuracy, consistency, timeliness), define validation rules, and track defects through closure.
• Ensure adherence to data policies (retention, privacy, information security) and audit readiness with appropriate evidence and artifacts.
• Contribute to standardized metrics and reporting for compliance and delivery governance.

Must‑Have

• 5–7 years of experience as a Data Analyst (or similar) in complex enterprise environments.
• Strong SQL skills (data extraction, joins, window functions, performance basics) and hands‑on experience with Power BI (data modeling, DAX/Power Query, visuals).
• Demonstrated experience analyzing application data structures, mapping data flows, and working with technical SMEs.
• Proven ability to translate ambiguous business problems into clear analytical outputs and backlog items (Epics/Stories/AC).
• Excellent documentation, communication, and stakeholder management skills; highly organized, proactive, analytical, and detail‑oriented.
• Working knowledge of Agile frameworks (Scrum/Kanban) and delivery tools (Jira/Confluence).

Nice‑to‑Have

• Exposure to cloud data platforms (e.g., Azure ADLS/ADF/Synapse/Databricks, or AWS/GCP equivalents).
• Awareness of data retention/privacy (e.g., GDPR principles), data governance, and metadata/lineage tools (e.g., Collibra).
• Experience with Python for analysis/automation (pandas), and Excel advanced features (Power Query, advanced formulas).
• Telecom or large‑scale consumer services domain experience.

Education & Certifications

• Bachelor’s degree in Computer Science, Information Systems, Mathematics, Statistics, Engineering, or equivalent practical experience.
• Certifications (nice‑to‑have): Microsoft Power BI, Azure Fundamentals, PSM/CSM.

Tools & Technologies

• Data & BI: SQL, Power BI (DAX, Power Query), Excel.
• Collaboration & Delivery: Jira, Confluence, MS Teams, SharePoint.

(Optional/Plus): Python, Azure (ADF/ADLS/Synapse/Databricks), Git.