Data Analyst

About Us

In our journey of Snapp Kitchen, we are pioneers in the Cloud Kitchen Market in Iran, which is a new form of catering and restaurant services where there are no restaurants, tables, chairs, or even customers in person; Cloud kitchens only use online food delivery services. What matters for us is to deliver results through teamwork, mutual trust, working in a friendly and professional work environment, and encountering challenges in our business with problem-solving and a creative attitude.

We are willing to have you on our journey if you are eager too, now it's the time!

Job Description

As a Data Analyst, your mission is to perform comprehensive data analyses, develop insightful reports, and support strategic decision-making by transforming complex data into actionable insights.

Data Analysis: Conduct detailed analyses to identify trends, patterns, and correlations within datasets.

Report Development: Create and maintain interactive dashboards and reports using data visualization tools to present key metrics to stakeholders.

Data Modeling: Develop and manage data models to support analytics and reporting needs.

Data Preparation: Perform data cleaning and transformation to prepare datasets for analysis.

Insight Generation: Translate data findings into actionable business insights and recommendations.

The Data Analyst role involves significant interaction with multiple teams, requiring the ability to understand diverse business needs and translate them into data-driven solutions. Key complexities include:

1.            Cross-Departmental Collaboration: Engaging with different departments to gather requirements and deliver relevant insights.

2.            Data Integration: Integrating data from multiple sources to provide comprehensive analysis.

3.            Problem-Solving: Addressing complex data-related issues and providing effective solutions.

Requirements

- The ideal candidate is fluent in Python (pandas) or R and applies advanced data‑cleaning techniques—imputing missing values, detecting outliers, and optimizing data quality end‑to‑end. 

- You’ll be responsible for merging, transforming, and harmonizing data from multiple internal and external sources to create robust, analysis‑ready datasets. 

- Strong inferential‑statistics skills are essential; you should routinely design and run hypothesis tests, build confidence intervals, and develop regression models to surface key drivers of performance. 

- A solid working knowledge of core machine‑learning techniques—such as classification, clustering, and predictive modeling—is required to turn historical data into forward‑looking insights. 

- You must be able to interpret statistical and ML outputs and translate them into concise, actionable recommendations that shape business strategy. 

- Proven expertise in crafting interactive dashboards in Tableau or Power BI is critical; your visuals should blend multiple data streams, include dynamic filters and drill‑downs, and answer specific stakeholder questions. 

- Advanced SQL (or comparable querying skills) is a must for efficiently extracting and manipulating large, complex data sets, and clear communication skills are needed to present findings to both technical and non‑technical audiences.