4 Analysis Manager jobs in the United Arab Emirates

Role Overview

We are seeking a highly analytical and strategic FP&A Manager to support financial planning, budgeting, forecasting, and decision-making for a real estate development company. The role requires strong financial modeling skills, a deep understanding of real estate finance, and the ability to provide actionable insights to senior management.

• Lead the budgeting, forecasting, and financial planning processes for real estate projects and overall company operations.
• Prepare financial models, scenario analysis, and profitability forecasts for new and ongoing development projects.
• Analyze financial performance, track KPIs, and provide variance analysis to support strategic decisions.
• Collaborate with project managers, accounting, and operations teams to ensure accurate financial reporting.
• Develop dashboards and reports to provide actionable insights to senior management and stakeholders.
• Support investment analysis, feasibility studies, and capital allocation decisions for new development projects.
• Ensure compliance with accounting standards, corporate policies, and internal controls.
• Assist in presentations to investors, lenders, and executive leadership.

• Bachelor’s degree in Finance, Accounting, Economics, or related field (Master’s or MBA preferred).
• 5–8 years of experience in FP&A, preferably in real estate development, construction, or property investment.
• Strong financial modeling, budgeting, and forecasting skills.
• Proficiency in Excel, ERP systems, and financial reporting tools (e.g., SAP, Oracle, Yardi).
• Excellent analytical, problem-solving, and communication skills.
• Ability to work under tight deadlines and manage multiple projects simultaneously.

• Financial Planning & Analysis (FP&A)
• Real Estate Project Finance
• Budgeting & Forecasting
• Financial Modeling & Valuation
• KPI Tracking & Reporting
• Investment & Feasibility Analysis
• ERP & Reporting Tools

The Senior Business Analyst leads a team to undertake the review and analysis of the organisation’s business intentions, services, processes and information needs to identify changes that lead to business improvements.

Department: Project Management

Compensation: AED 21,667 P/M

• Enhance business systems by providing accurate information about business needs and priorities.
• Analyse and consolidate information to develop business cases to support achievement of business objectives.
• Extract data reports and review information to identify trends, system pain points and opportunities for business improvement across the business.
• Provide plain language advice on technical issues to non-technical audiences.
• Work with key stakeholders to identify how changing business requirements may be delivered with existing solutions.
• Coordinate process improvement test cases, liaising with DSG and client business units, evaluating and reporting on potential process improvement initiatives and instituting systems changes.
• Develop and use information material such as process descriptions, checklists, templates and guides to assist staff with implementing defined processes.
• Guide and support team members to provide customer-focused services.

• A bachelor’s or master’s degree in computer science or a related field is a good starting point for this position.
• Escalate issues, keep informed, advise and receive feedback.
• Inspire and motivate team, provide direction and manage performance.
• Mentor, lead and support the team and share information and research.
• Ensure consistent and coordinated customer services through teamwork and collaboration.
• Resolve issues and provide solutions to problems.
• Provide information regarding agency sector-wide rules and standards.
• Represent the organisation in an honest, ethical and professional way and encourage others to do so.
• Demonstrate professionalism to support a culture of integrity within the team/unit.
• Ensure that others understand the legislation and policy framework within which they operate and act to prevent and report misconduct, illegal and inappropriate behaviour.

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