Junior Data Analyst

Role overview

Reporting and business intelligence

• Prepare and maintain daily, weekly, and monthly performance reports covering revenue, trips, fleet utilization, 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.
• Prepare accurate datasets, summaries, and preliminary insights; standardize metric definitions and documentation.

Analysis and data quality

• Analyze customer behavior, fleet performance, productivity, and operational trends; conduct ad hoc analyses with other departments.
• Identify trends, anomalies, correlations, and opportunities for improvement; assist with forecasting, customer segmentation, and retention analysis.
• Present findings clearly and explain their business implications.
• Write and optimize SQL queries; validate data accuracy and investigate source discrepancies.
• Maintain reusable queries and documented business logic, and apply data cleaning, transformation, and reconciliation techniques.

Automation and analytics infrastructure

• Identify repetitive reporting and data preparation work for automation and develop workflows using SQL, Python, Excel, or other suitable tools.
• Improve reporting refresh processes and analytical pipelines, reduce manual dependencies, and improve data reliability.
• Document automated workflows, dependencies, and validation checks.
• Support analytical data models, centralized datasets, data warehousing, SQL transformations, reconciliation, pipeline testing, and reusable datasets.
• Collaborate with technical teams to improve data accessibility, quality, and reporting performance.

Required qualifications and 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 visualization 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, attention to detail, stakeholder communication, and willingness to learn.

Preferred qualifications

• Preferred but not mandatory: PostgreSQL or similar relational databases; Python libraries such as pandas and NumPy; SQL window functions and basic query optimization.
• Preferred but not mandatory: data modeling, ETL/ELT, automated pipelines, cloud data warehouses such as Snowflake, BigQuery, or ClickHouse.
• Preferred but not mandatory: Git, APIs, scheduled data processing, statistical analysis, forecasting, or customer analytics.
• Preferred but not mandatory: exposure to mobility, transportation, automotive, subscription, or consumer-platform businesses.