Data Analyst

Job Title: Junior Data Analyst

Summary:
The Junior Data Analyst will support the collection, cleaning, analysis, and reporting of data to help the business make informed, datadriven decisions. The role is suitable for fresh graduates or earlycareer professionals with strong analytical skills and an interest in turning data into insights.

Key responsibilities

• Collect and compile data from multiple internal and external sources (e.g. CRM, spreadsheets, databases).

• Clean, validate, and transform raw data to ensure accuracy, completeness, and consistency.

• Perform basic exploratory data analysis to identify trends, patterns, and anomalies.

• Build and maintain regular reports and dashboards for business stakeholders (using Excel and/or BI tools).

• Support senior analysts with adhoc analysis and simple statistical queries.

• Document data definitions, data sources, and reporting logic for repeatability.

• Collaborate with business teams (e.g. marketing, operations, finance) to understand their data needs and translate them into simple analysis tasks.

• Monitor data quality and flag issues or inconsistencies to relevant teams.

• Stay updated on basic analytics tools, functions, and best practices relevant to the role.

Required qualifications and skills

• Bachelor's degree in Statistics, Mathematics, Data Science, Computer Science, Economics, Engineering, or a related quantitative field (or finalyear student/intern equivalent).

• Strong analytical and problemsolving skills, with comfort working with numbers and large datasets.

• Good knowledge of spreadsheets (Excel or Google Sheets), including formulas, filters, pivot tables, and charts.

• Basic exposure to at least one query or scripting language (e.g. SQL, Python, or R) is preferred willingness to learn is essential.

• Familiarity with any data visualisation or BI tools (e.g. Power BI, Tableau, Looker Studio) will be an advantage.

• Strong attention to detail and commitment to data accuracy.

Nicetohave (optional)

• Academic projects, internships, or personal portfolio demonstrating data analysis work (e.g. GitHub, Kaggle, dashboards, or case studies).

• Basic understanding of statistics (e.g. averages, distributions, correlations, hypothesis testing) and how these apply to business questions.

• Exposure to data from domains such as marketing, ecommerce, operations, or HR analytics.