Job Responsibilities/ Requirements
We are looking for a Data Analyst who is passionate about turning data into clear insights and intuitive dashboards. In this role, you will work closely with product, operations, and business stakeholders to translate questions and problems into reports, analyses, and visualizations that drive better decisions.
Job Responsibilities:
- Design, build, and maintain interactive dashboards and reports in tools such as Power BI, Tableau, or Looker to support business monitoring and decision-making.
- Perform detailed data analysis and deep dives to identify trends, anomalies, and root causes across key business metrics.
- Work with stakeholders to translate business questions into well-structured analytical problems, define success metrics, and propose measurement approaches.
- Write efficient SQL to extract, transform, and aggregate data from data warehouses or data lakes for analysis and reporting.
- Validate data quality, investigate data issues, and collaborate with data engineering teams to improve data pipelines and definitions.
- Present analytical findings and recommendations through clear visualizations, documents, and presentations tailored to technical and non-technical audiences.
- Contribute to the development and documentation of standardized metrics, business definitions, and reporting processes.
- Proactively identify opportunities to improve existing dashboards, automate recurring reports, and enhance analytical frameworks.
- Any Ad hoc duties as assigned.
Job Requirements:
- Bachelor's degree in Statistics, Mathematics, Computer Science, Engineering, Economics, Business Analytics, or a related quantitative field.
- Preferably 2-3 years of hands-on experience as a Data Analyst, Business Analyst, or similar analytics role.
- Strong proficiency in at least one modern BI/dashboarding tool (e.g., Power BI, Tableau, Looker, or similar) including data modeling, visual design, and dashboard performance optimization is preferred.
- Advanced SQL skills, with experience querying large datasets from relational databases, data warehouses, or data lakes.
- Solid experience with data analysis in Python or R (e.g., using Pandas, NumPy, basic statistical and analytical libraries) is a strong plus.
- Good understanding of data modeling concepts, joins, aggregations, and handling data quality issues.
- Experience conducting deep dive analyses to answer ad-hoc business questions, quantify impact, and identify root causes.
- Familiarity with A/B testing, experimentation, or basic statistical methods (e.g.,hypothesis testing, confidence intervals) is an advantage.
- Experience working in domains such as ride-hailing, logistics, e-commerce, or other high-growth, data-rich environments is a plus but not required.
- Strong business-oriented thinking, with the ability to connect data insights to real operational or product implications
- Excellent problem-solving skills, with a structured approach to breaking down complex questions and validating assumptions.
- High attention to detail and data accuracy, with a habit of checking and validating your own work.
- Effective communication skills, including the ability to explain analytical approaches and insights clearly to non-technical stakeholders.
