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Data Analyst
METRO/MAKRO
Pune, Maharashtra, IndiaOn Site
Original Job Summary
About the Role
The Data Analyst will build and maintain dashboards, conduct deep data analysis, and lead experimentation practices for a heavy ML-driven product at Metro Global Solution Center (MGSC). The role involves collaborating with product teams to drive data-driven decisions and improve KPIs for international operations.
Key Responsibilities
- Build and maintain dashboards for KPI tracking.
- Dig into data to uncover new use cases.
- Lead the team’s experimentation practice.
- Identify and resolve data quality issues.
- Collaborate with product teams and stakeholders.
- Develop new KPIs as needed.
- Educate teams on data usage.
Qualifications
Applicants should hold a Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related fields along with strong analytical, communication, and technical skills.
Technical Skills
- Advanced SQL and query optimization (BigQuery preferred).
- Experience with dashboarding tools such as Looker Studio, Microstrategy, Tableau, or Looker.
- Proven A/B testing experience including experiment design and statistical evaluations.
- Proficiency in Python, particularly using Jupyter notebooks.
- Solid understanding of Git version control.
Nice-to-Have
- Experience in cross-functional collaboration.
- Understanding of recommender systems.
- Experience with Google Cloud Platform analytical technologies.
Key skills/competency
Data Analysis, SQL, Dashboarding, Python, A/B Testing, Statistics, Data Quality, Experimentation, Collaboration, Git
How to Get Hired at METRO/MAKRO
🎯 Tips for Getting Hired
- Research Metro/MAKRO's culture: Explore the company's mission and projects.
- Tailor your resume: Highlight SQL, Python, and dashboarding skills.
- Prepare data experiments: Showcase A/B testing and statistical analysis.
- Demonstrate collaboration: Provide examples working with cross-functional teams.
📝 Interview Preparation Advice
Technical Preparation
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Review advanced SQL queries.
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Practice Python analysis in Jupyter.
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Study dashboarding tool functionalities.
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Brush up on statistical testing methods.
Behavioral Questions
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Describe handling challenging data issues.
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Explain collaboration with cross-functional teams.
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Share a successful experiment story.
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Discuss adapting to feedback quickly.