Data Analyst

Position: Data Analyst

Location: Sydney

Duration: 6 months

Must Have Skills:

• Strong proficiency in SQL for data extraction, querying, and analysis
• Hands‑on experience with data visualization tools such as Power BI, Tableau, or similar
• Advanced knowledge of MS Excel (pivot tables, formulas, data analysis features)
• Solid understanding of data analysis concepts, KPIs, and business metrics
• Ability to analyze data and translate insights clearly to business stakeholders

Nice to Have Skills:

• Experience with cloud data platforms such as Azure, AWS, or GCP
• Working knowledge of Python or R for data analysis and automation
• Exposure to data modeling and ETL concepts
• Familiarity with Agile/Scrum delivery environments
• Experience working with cross‑functional business and technical teams

Detailed Job Description

The Data Analyst will be responsible for collecting, analyzing, and interpreting structured and semi‑structured data to support informed business decision‑making. The role involves working closely with stakeholders to understand business requirements, translating them into analytical solutions, and delivering insights through dashboards, reports, and presentations. The analyst is expected to ensure data accuracy, integrity, and consistency while enabling data‑driven recommendations aligned with business objectives.

The role also includes identifying trends, patterns, and anomalies in data, supporting reporting automation initiatives, and collaborating with data engineering and business teams to enhance data availability and usability. The candidate should be comfortable working in fast‑paced environments and managing multiple analytical priorities.

Top 3 responsibilities

Data Analysis & Reporting:

Analyze large datasets to identify trends, patterns, and insights; create dashboards, reports, and visualizations using tools such as Power BI, Tableau, or Excel.

Business Stakeholder Support:

Work closely with business stakeholders to gather requirements, translate them into analytical outputs, and provide actionable insights to support decision‑making.

Data Quality & Process Improvement:

Ensure data accuracy and consistency by performing data validation checks and contribute to improving reporting processes and automation where possible