Location: Dubai, UAE
Experience: 0–2 years
Reporting to: Data Analyst / Analytics Lead
Employment: Full-time
About the Role
Udrive is looking for a curious, analytical and detail-oriented Junior Data Analyst to support the company's growing data analytics function.
The role will focus on business performance reporting, data analysis, dashboard development, data quality and process automation across commercial, operational and customer-related functions.
The successful candidate will work closely with the Analytics Lead and cross-functional stakeholders to transform complex datasets into accurate, actionable business insights.
Beyond day-to-day reporting, the role offers opportunities to contribute to data modelling, automated reporting pipelines and the development of Udrive's future analytics infrastructure.
Key Responsibilities
1. Business Intelligence & Performance Reporting
Prepare and maintain recurring daily, weekly and monthly performance reports covering revenue, trip activity, fleet utilisation, customer acquisition and operational efficiency.
Develop, maintain and improve interactive Tableau dashboards for management and operational teams.
Monitor business KPIs, investigate unexpected changes and identify potential performance issues.
Support management reporting by preparing accurate datasets, summaries and preliminary insights.
Ensure reporting consistency through standardised metric definitions and documentation.
2. Data Analysis & Business Insights
Analyse customer behaviour, fleet performance and productivity and operational trends.
Conduct ad-hoc analyses and work cross-functionally to support other departments.
Identify trends, anomalies, correlations and potential opportunities for business improvement.
Assist with forecasting, customer segmentation, retention analysis and other analytical projects.
Present findings clearly, explaining both the results and their business implications.
3. SQL, Data Preparation & Quality Assurance
Write and optimise SQL queries to extract, join, aggregate and analyse data from relational databases.
Validate data accuracy, identify inconsistencies and investigate discrepancies between data sources.
Maintain reusable queries, reporting datasets and documented business logic.
Apply appropriate data-cleaning, transformation and reconciliation techniques.
4. Automation & Analytics Process Improvement
Identify repetitive reporting and data preparation activities that can be automated.
Develop and maintain basic automation workflows using SQL, Python, Excel or other appropriate tools.
Support the improvement of reporting refresh processes and analytical data pipelines.
Assist with reducing manual reporting dependencies and improving data reliability.
Document automated workflows, dependencies and validation checks.
5. Data Infrastructure & Analytics Development
Support initiatives involving analytical data models, centralised datasets and data warehousing.
Assist with SQL transformations, data reconciliation, pipeline testing and documentation.
Collaborate with the technical team on improvements to data accessibility, quality and reporting performance.
Contribute to the development of scalable and reusable analytical datasets.
Required Qualifications & Skills
Bachelor's degree in Data Analytics, Computer Science, Statistics, Mathematics, Economics, Engineering or a related quantitative field.
0–2 years of relevant experience, including internships, academic projects or equivalent practical experience.
Good working knowledge of SQL, including joins, aggregations, subqueries and common table expressions.
Proficiency in Microsoft Excel, including PivotTables, lookup functions and data analysis.
Working knowledge of at least one analytical or visualisation tool, preferably Tableau or Power BI.
Basic proficiency in Python for data analysis or automation, or demonstrated ability to learn it.
Strong analytical reasoning, problem-solving skills and attention to detail.
Ability to communicate findings clearly to both technical and non-technical stakeholders.
Willingness to learn new technologies and understand business processes.
Preferred Qualifications
Experience with PostgreSQL or similar relational databases.
Familiarity with Python libraries such as pandas and NumPy.
Understanding of SQL window functions and basic query optimisation.
Exposure to data modelling, ETL/ELT processes or automated data pipelines.
Familiarity with cloud data warehouses or analytical databases such as Snowflake, BigQuery or ClickHouse.
Experience with Git, APIs or scheduled data-processing workflows.
Knowledge of statistical analysis, forecasting or customer analytics.
Exposure to mobility, transportation, automotive, subscription or consumer-platform businesses.
Preferred qualifications are advantageous but are not mandatory.
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