Data Analyst

Roles and responsibilities

Who are we looking for?

• Able to uncover insights and offer recommendations using statistically sound techniques
  
• Strong knowledge of programming languages, with a focus on machine learning and advanced analytics (SQL/R/Python)
  
• Highly driven self-starters who can communicate complex ideas in a clear and effective manner
  
• Excellent organizational skills ; Have the ability to prioritize workload whilst being resilient and being able to cope well under pressure and meeting tight deadlines
  
• Strong grasp of English. Proficiency in Arabic would be a plus
  
• The ability and willingness to travel
  
  
• Qualifications





• Passionate about data, analytics and technology
  
• Minimum of Bachelor's degree or higher in marketing, economics, mathematics, or technical specialty
  
• Technical understanding of how digital analytics and tag management solutions are deployed
  
• Ability to complete complex tag deployment within tools like Dynamic Tag Manager and Google Tag Manager
  
• Knowledge of web analytics and tag management solutions and differences between providers (ex: Adobe, Google, Tealium, Ensighten, etc.)
• Data Presentation: Clearly presenting findings to stakeholders, whether through written reports, presentations, or meetings.
• Cross-Department Collaboration: Communicating effectively with cross-functional teams such as marketing, finance, and operations to understand data needs and support decision-making.
• Data Storytelling: Using data to tell a compelling story, translating complex data insights into clear business implications.
• Solid Knowledge of data integration techniques and processes (Data matching & key vendors, data fusion & key partners, etc.)

Desired candidate profile

1. Data Collection and Data Management

• Data Gathering: Collecting data from various sources, such as databases, spreadsheets, APIs, and external datasets.
• Data Cleaning: Cleaning and preprocessing data by handling missing values, correcting inconsistencies, and ensuring data quality.
• Data Storage and Management: Organizing and storing data efficiently, ensuring it's easily accessible and appropriately categorized for analysis.

2. Statistical Analysis and Interpretation

• Statistical Methods: Applying statistical methods (e.g., mean, median, standard deviation, regression analysis) to analyze datasets and identify patterns or trends.
• Hypothesis Testing: Conducting hypothesis testing to validate assumptions and draw conclusions about the data.
• Data Modeling: Building statistical or machine learning models to predict trends or outcomes (e.g., regression models, classification models).

3. Data Visualization

• Visualization Tools: Using tools like Tableau, Power BI, Matplotlib, or ggplot2 (for Python/R) to create meaningful charts, graphs, and dashboards.
• Report Generation: Creating visual reports and dashboards that convey complex insights in an easily understandable format for stakeholders.
• Storytelling with Data: Presenting data findings in a narrative format, providing context, and explaining the impact of the data insights on the business.

4. Database Management and Querying

• SQL Proficiency: Writing and optimizing SQL queries to extract data from relational databases (e.g., MySQL, PostgreSQL, SQL Server).
• NoSQL Databases: Understanding and working with non-relational databases (e.g., MongoDB, Cassandra) when dealing with unstructured data.
• Data Warehousing: Familiarity with data warehousing concepts, including designing and managing large-scale databases for long-term storage.

5. Data Cleaning and Preprocessing

• Data Normalization: Ensuring data is in a consistent format, normalizing values, and transforming data as required for analysis.
• Data Validation: Validating the accuracy of data through error checks and cross-referencing data from multiple sources.
• Handling Outliers: Identifying and dealing with outliers or anomalies in data that might skew analysis.

6. Technical Skills

• Programming Languages: Proficiency in programming languages such as Python or R to manipulate data, perform analysis, and implement models.
• Data Analysis Libraries: Familiarity with data manipulation and analysis libraries like Pandas, NumPy, SciPy (Python), or dplyr, ggplot2 (R).
• Automation Tools: Knowledge of automation tools and techniques to streamline repetitive tasks, such as data collection or report generation.

7. Business Acumen and Domain Knowledge

• Understanding Business Requirements: Collaborating with business stakeholders to understand their data needs and providing relevant insights that drive decision-making.
• KPI Monitoring: Monitoring and analyzing key performance indicators (KPIs) for the business or department to identify areas for improvement.
• Domain Expertise: Having an understanding of the industry or field in which the organization operates, so you can contextualize the data and make relevant recommendations.

8. Data Integrity and Security

• Data Governance: Ensuring that the data analysis process follows established data governance policies and practices, including data privacy regulations (e.g., GDPR, CCPA).
• Security Practices: Implementing security measures to protect sensitive data, especially when dealing with customer or financial data.
• Ethical Data Handling: Ensuring data analysis is conducted ethically, with respect for privacy and confidentiality.