9 Google Analytics jobs in the United Arab Emirates

The Director of Data Analysis is responsible for collecting, processing, and analysing real estate data from various sources, with the aim of providing accurate, data-driven insights that support strategic decision-making in the real estate sector in United Arab Emirates. The role focuses on enhancing market transparency, developing sector-wide performance indicators, and supporting policy formulation and investment planning based on data.

Responsibilities

• Collecting and analyzing real estate data from multiple sources
• Processing and cleaning data to ensure its accuracy, consistency, and readiness for analysis
• Analyzing real estate data to extract and update the real estate index, which enhances market transparency
• Preparing reports and dashboards to support strategic decision-making
• Developing sector performance indicators (e.g., price indices, supply and demand, occupancy rates, etc.)
• Supporting policy development and investment planning by providing data-driven recommendations
• Contributing to real estate market studies and identifying market trends to support and update strategic urban development plans
• Collaborating with government entities and investors to provide transparent and accurate insights into the real estate market

Requirements

• Bachelor's degree in Economics, Statistics, Business Administration, Data Analysis, or a related field.
• 5 years of experience in data analysis or market research, with knowledge of the real estate market.
• Certifications in data analysis or economics are not required but are preferred
• Certifications in Data Analysis, such as RICS or CF

We are seeking a skilled professional to lead our data analysis team. As a Lead Data Analyst, you will be responsible for overseeing a team of analysts and collaborating with developers and stakeholders to ensure software meets business needs.

• Manage a team of data analysts
• Create data marts and data lakes using mappings
• Design user interfaces and dashboards for data visualization and interaction
• Test and validate software and data products for accuracy and reliability

• Bachelor's or master's degree in Computer Science, IT, or related field
• Experience with data processing and analysis using Data Bricks
• Expert-level proficiency in SQL, Python, SAS/R, Spark
• Deployment experience in databases, server/cloud environments (AWS, Azure), APIs, ODBCs, web apps

• Career Growth: Opportunities for professional development and advancement
• Performance-Based Compensation: Competitive pay linked to performance
• Inclusive Culture: Join a collaborative team that values innovation and every voice

This is an exciting opportunity to work with a talented team and contribute to our success.

Effective Business Decisions Using Data Analysis

ID 257

Course: Effective Business Decisions Using Data Analysis

This interactive, applications-driven 5-day course will highlight the added value that data analytics can offer a professional as a decision support tool in management decision making. It will show the use of data analytics to support strategic initiatives; to inform on policy information; and to direct operational decision making. The course will emphasize applications of data analytics in management practice; focus on the valid interpretation of data analytics findings; and create a clearer understanding of how to integrate quantitative reasoning into management decision making. Exposure to the discipline of data analytics will ultimately promote greater confidence in the use of evidence-based information to support management decision making.

• Discussions on applications of data analytics in management
• The importance of data in data analytics
• Applying data analytical methods through worked examples
• Focusing on management interpretation of statistical evidence
• How to integrate statistical thinking into the work domain

• Explain the scope and structure of data analytics.
• Apply a cross-section of useful data analytics.
• Interpret meaningfully and critically assess statistical evidence.
• Identify relevant applications of data analytics in practice.

• Professionals in management support roles
• Analysts who typically encounter data/analytical information regularly in their work environment
• Those who seek to derive greater decision-making value from data analytics

This course will utilise a variety of proven adult learning techniques to ensure maximum understanding, comprehension, and retention of the information presented. The daily workshops will be highly interactive and participative. This involves regular discussion of applications as well as hands-on exposure to data analytics techniques using Microsoft Excel. Delegates are strongly encouraged to bring and analyse data from their own work domain. This adds greater relevancy to the content. Emphasis is also placed on the valid interpretation of statistical evidence in a management context.

• Day One: Setting the Statistical Scene in Management
  • Introduction; The quantitative landscape in management
  • Thinking statistically about applications in management (identifying KPIs)
  • The integrative elements of data analytics
  • Data: The raw material of data analytics (types, quality, and data preparation)
  • Exploratory data analysis using Excel (pivot tables)
  • Using summary tables and visual displays to profile sample data
• Day Two: Evidence-based Observational Decision Making
  • Numeric descriptors to profile numeric sample data
  • Central and non-central location measures
  • Quantifying dispersion in sample data
  • Examine the distribution of numeric measures (skewness and bimodal)
  • Exploring relationships between numeric descriptors
  • Breakdown analysis of numeric measures
• Day Three: Statistical Decision Making – Drawing Inferences from Sample Data
  • The foundations of statistical inference
  • Quantifying uncertainty in data – the normal probability distribution
  • The importance of sampling in inferential analysis
  • Sampling methods (random-based sampling techniques)
  • Understanding the sampling distribution concept
  • Confidence interval estimation
• Day Four: Statistical Decision Making – Drawing Inferences from Hypotheses Testing
  • The rationale of hypotheses testing
  • The hypothesis testing process and types of errors
  • Single population tests (tests for a single mean)
  • Two independent population tests of means
  • Matched pairs test scenarios
  • Comparing means across multiple populations
• Day Five: Predictive Decision Making - Statistical Modeling and Data Mining
  • Exploiting statistical relationships to build prediction-based models
  • Model building using regression analysis
  • Model building process – the rationale and evaluation of regression models
  • Data mining overview – its evolution
  • Descriptive data mining – applications in management
  • Predictive (goal-directed) data mining – management applications

About the opportunity

The Growth Data Science Director will lead and accelerate growth strategies across marketing optimization, product initiatives (e.g., churn reduction), ecosystem plays, and ads revenue for talabat and Quick commerce. This role requires collaboration with product teams, senior marketing, and partner leadership to drive data-driven decisions and business growth.

What’s On Your Plate?

• Data Science: Define and execute data strategies aligned with company goals, leading cross-functional teams to develop innovative data-driven products and insights that impact business outcomes.
• Strategic Leadership and Business Acumen: Demonstrate strategic leadership, articulate vision, and incorporate emerging trends and technologies to add value.
• Collaboration and Influence: Work with stakeholders including executives, product managers, engineers, and external partners; represent the organization at industry events.
• Resource Management: Manage teams, budgets, and resources; prioritize projects; coach and mentor managers.
• Strategic Hiring and Talent Development: Recruit, develop career paths, and provide feedback to team members.
• Organizational Change Management: Lead change initiatives, communicate effectively, and manage resistance.

What you need to be successful

• 8-10+ years in data science, with leadership experience in managing teams or consulting.
• Ph.D. or Master’s in relevant fields such as computer science, statistics, or data science.
• Proven leadership in fostering high-performance teams and applying data science skills to create impactful products and insights.
• Deep knowledge of statistics, causality, experimentation, and modeling.
• Business acumen with experience in quantifying impact through data initiatives.
• Strategic thinking and ability to develop data-driven roadmaps aligned with organizational goals.

Who we are

Since 2004, talabat has been Kuwait’s leading on-demand food and Q-commerce app, serving eight countries. We leverage technology to simplify life, optimize operations, and provide earning opportunities. We foster a high-performance culture, value authenticity, and are proud of our awards and diverse team of over 6,000 Talabaty committed to making a difference.

Data Management, Manipulation and Analysis using Excel

ID 473

Course: Data Management, Manipulation and Analysis using Excel

This course is aimed at professionals who have, or will soon have, responsibility for managing and manipulating data using MS Excel on a day-to-day basis. The course assumes zero knowledge, begins with an introduction to the Excel environment, and ends with delegates being skilled in using 50+ MS Excel functions, sophisticated data management, and charting techniques, and advanced data analysis capability.

This course will feature:

• Advanced data analysis
• Both textual and numerical data
• Forecasting
• Advanced charting

By the end of this course, participants will be able to:

• Analyse relationships across information and data using MS Excel.
• Generate data forecasts using MS Excel.
• Organise your company’s data in a more structured manner.
• Analyse your data effectively using various MS Excel techniques.
• Select the appropriate chart for your data.

This course is suitable for a wide range of professionals but will greatly benefit:

• Administrators using MS Excel at a very basic level
• Administrators with a need to improve data management techniques utilising MS Excel
• New Administrative Staff with no prior knowledge of MS Excel
• HR professionals seeking to use MS Excel to analyse employee data and inventory data
• Oil and Gas, telecommunications, and electricity industry employees looking to improve their data management and data representation skills

This course will utilise a variety of proven adult learning techniques to ensure maximum understanding, comprehension, and retention of the information presented. The course will be split up into themes with a series of exercises based on each theme. The approach will also be incremental with each session building on prior knowledge. Each delegate will be introduced to practical, hands-on learning using MS Excel.

Delegates can bring their own Windows or Mac OS laptop to the sessions, for them to be comfortable with the environment and version of MS Excel that they will be working on.

Day One: An Introduction to the MS Excel Environment

• Cell referencing, cell formatting, and entering formula
• Copy and pasting
• Introductory charts

Day Two: Using MS Excel Functions for Fundamental Data Analysis

• Use of text function, FIND(), LEN(), LEFT(), RIGHT() and &
• Use of count functions, COUNTA(), COUNTIF(), COUNTIFS() and SUMIF()
• Basic statistical functions, Max and Average
• Filtering, sorting, and use of conditional formatting

Day Three: Intermediate MS Excel Functions

• Use of VLOOKUP() and HLOOKUP()
• Date functions, YEAR(), MONTH(), DAY(), YEARFRAC()
• Selecting appropriate charts
• Introduction to Pivot tables

Day Four: Carrying out Statistical Analysis using MS Excel

• Using MS Excel to calculate mean, mode, and median
• The difference between the various standard deviation and variance function in MS Excel
• Using MS Excel to examine inter-dependency
• Drawing histograms in MS Excel
• Introduction to Data Analysis functions

Day Five: What if and Scenario Analysis Using MS Excel

• Naming cells in MS Excel
• Linking cells together to undertake scenario analysis
• Introduction to solver
• Advanced charting
• Sharing MS Excel output with other office formats

European Quality Training and Management Consultancy

At European Quality Training and Management Consultancy, we provide high-quality training and consultancy services to develop future leaders. With a team of skilled experts, we tailor programs to meet the needs of public and private sectors, grounded in quality, ethics, and social responsibility. Our client-focused approach ensures professionalism and sustainable outcomes.

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A globally recognised and leading technology firm, at the forefront of innovation and digital transformation.

• Lead the integration, management, and utilisation of data from various digital marketing channels (e.g., paid media, SEO, social media, web analytics) within a CDP.
• Develop comprehensive dashboards, reports, and visualisations to monitor KPIs and provide actionable insights on campaign effectiveness, website performance, and customer engagement.
• Conduct deep-dive analyses to identify trends, opportunities, and challenges in digital marketing performance, providing recommendations for optimisation and strategic adjustments.
• Partner closely with marketing strategists, campaign managers, product teams, and data engineering to ensure data accuracy, accessibility, and alignment with business objectives.
• Implement and refine attribution models to understand the true impact of different marketing touchpoints on conversions and customer lifetime value.
• Support the development of personalisation strategies by leveraging CDP data to segment audiences and deliver tailored marketing messages.

• Bachelor's degree or equivalent practical experience in Marketing, Data Science, Statistics, Computer Science, or a related analytical field.
• Minimum of 6+ years of progressive experience in digital marketing analytics, with a strong focus on data analysis and insight generation.
• Strong proficiency in SQL for data extraction and manipulation.
• Experience with web analytics platforms (e.g., Google Analytics 4, Adobe Analytics) and tag management systems (e.g., Google Tag Manager).
• Solid understanding of digital marketing channels, metrics, and optimisation strategies (e.g., SEM, SEO, Social Media, Display, Email Marketing).
• Excellent analytical, problem-solving, and critical thinking skills, with an ability to translate raw data into clear, actionable business recommendations.

A globally recognised and leading technology firm, at the forefront of innovation and digital transformation.

• Lead the integration, management, and utilisation of data from various digital marketing channels (e.g., paid media, SEO, social media, web analytics) within a CDP.
• Develop comprehensive dashboards, reports, and visualisations to monitor KPIs and provide actionable insights on campaign effectiveness, website performance, and customer engagement.
• Conduct deep-dive analyses to identify trends, opportunities, and challenges in digital marketing performance, providing recommendations for optimisation and strategic adjustments.
• Partner closely with marketing strategists, campaign managers, product teams, and data engineering to ensure data accuracy, accessibility, and alignment with business objectives.
• Implement and refine attribution models to understand the true impact of different marketing touchpoints on conversions and customer lifetime value.
• Support the development of personalisation strategies by leveraging CDP data to segment audiences and deliver tailored marketing messages.

• Bachelor's degree or equivalent practical experience in Marketing, Data Science, Statistics, Computer Science, or a related analytical field.
• Minimum of 6+ years of progressive experience in digital marketing analytics, with a strong focus on data analysis and insight generation.
• Strong proficiency in SQL for data extraction and manipulation.
• Experience with web analytics platforms (e.g., Google Analytics 4, Adobe Analytics) and tag management systems (e.g., Google Tag Manager).
• Solid understanding of digital marketing channels, metrics, and optimisation strategies (e.g., SEM, SEO, Social Media, Display, Email Marketing).
• Excellent analytical, problem-solving, and critical thinking skills, with an ability to translate raw data into clear, actionable business recommendations.