Data Analyst Resume Example & Writing Guide for GCC Jobs

Why Your Data Analyst Resume Needs a GCC Focus

The Gulf Cooperation Council countries are in the midst of a data revolution. Governments and enterprises across the UAE, Saudi Arabia, Qatar, Kuwait, Bahrain, and Oman are investing billions in digital transformation, and data analytics is at the centre of nearly every strategic initiative. The UAE's National Strategy for Artificial Intelligence 2031 explicitly positions data as a national asset, while Saudi Arabia's Vision 2030 and the National Data Management Office are driving massive data governance and analytics adoption across government and private sectors. This creates extraordinary demand for skilled data analysts who understand both the technical craft and the GCC business context.

Data analyst roles in the GCC differ from their counterparts in Western markets in several important ways. First, many GCC organizations are at an earlier stage of analytics maturity, meaning data analysts are often expected to build foundational capabilities — establishing data pipelines, creating reporting frameworks, training stakeholders, and evangelizing data-driven decision-making — rather than simply maintaining existing systems. This means your resume must demonstrate not just analytical skills, but the ability to build data infrastructure and culture from the ground up.

Second, the GCC business landscape spans unique industries that require domain-specific analytical expertise. Oil and gas analytics, Islamic banking and finance data, real estate market analysis, government service optimization, aviation and logistics data, and retail analytics for luxury and high-net-worth consumer segments are all areas where GCC employers need analysts who combine technical skills with sector knowledge. A data analyst who can speak to experience in these verticals has a distinct advantage over a technically strong but industry-agnostic candidate.

Third, data privacy and governance regulations are evolving rapidly across the GCC. The UAE issued Federal Decree-Law No. 45 of 2021 on Personal Data Protection, Saudi Arabia enacted the Personal Data Protection Law (PDPL), and other GCC states are developing similar frameworks. Data analysts must understand these regulations and demonstrate experience working within compliance frameworks — particularly for roles in financial services, healthcare, and government sectors where data sensitivity is highest.

The competitive landscape for data analyst positions in the GCC is intensifying. Major employers like Saudi Aramco, ADNOC, Emirates NBD, Careem, Noon, and government entities such as the Dubai Digital Authority and Saudi Data and AI Authority (SDAIA) are actively recruiting data professionals. Global consulting firms (McKinsey, BCG, Deloitte, PwC) with major GCC practices also hire data analysts extensively. Your resume must clearly communicate your technical capabilities, business acumen, and readiness to contribute to the data-driven transformation of the Gulf economy.

Key Sections Every Data Analyst Resume Must Include

Personal Information & Contact Details

Include your full name, nationality, visa status, phone number with GCC country code, professional email, LinkedIn profile, and — crucially for data analysts — your GitHub profile or portfolio website. A well-maintained GitHub repository with data projects, Jupyter notebooks, or Kaggle competition entries demonstrates practical skill in ways that a traditional resume cannot. Many GCC tech recruiters check GitHub profiles as part of their screening process.

Professional Summary

Write a concise 3-4 sentence summary that highlights your years of data analytics experience, core technical stack (Python, SQL, visualization tools), industries served, and key quantified achievements. Mention any GCC-specific experience or domain expertise prominently. Lead with your most impressive metric — revenue impact, cost savings, or efficiency gains from your analytical work.

Technical Skills

Organize your technical skills into clear categories: Programming Languages (Python, R, SQL), Visualization Tools (Power BI, Tableau, Looker), Database Technologies (PostgreSQL, MySQL, BigQuery, Snowflake), Cloud Platforms (AWS, Azure, GCP), Statistical Methods and Machine Learning frameworks, and relevant tools (Excel/Google Sheets advanced, Jupyter, Git). A structured skills section is critical for ATS parsing and for recruiters who screen based on specific technology requirements.

Work Experience

Reverse chronological format with 4-5 achievement-focused bullet points per role. Every bullet should follow the format: action verb + what you did + quantified business impact. Data analysts must connect technical work to business outcomes. "Built a Power BI dashboard" is weak; "Built a Power BI dashboard tracking AED 150M in monthly revenue across 12 product lines, reducing reporting time from 3 days to 4 hours and enabling weekly pricing decisions that improved margins by 8%" demonstrates value.

Projects & Portfolio

For data analysts, a dedicated projects section is highly valuable, especially for early-career professionals or those transitioning from adjacent fields. Include 2-3 projects with the business problem, your approach, tools used, and measurable outcome. GCC-relevant projects (oil price forecasting, real estate market analysis, Arabic NLP, e-commerce analytics) resonate strongly with regional employers.

Education & Certifications

Degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a quantitative field. Professional certifications from Google, Microsoft, AWS, and Tableau carry significant weight for data analyst roles. In the GCC, Microsoft certifications (Power BI Data Analyst Associate) are particularly valued as Power BI dominates the regional enterprise analytics market.

Top 10 Skills for Data Analyst in the GCC

1. SQL & Database Querying — SQL remains the foundational skill for any data analyst. The ability to write complex queries involving joins, window functions, CTEs, subqueries, and aggregations across relational databases is non-negotiable. GCC employers expect proficiency in PostgreSQL, MySQL, Microsoft SQL Server, and increasingly BigQuery and Snowflake for cloud data warehouse environments. Data analysts who can optimize query performance, understand indexing, and work with database administrators to design efficient schemas are particularly valued in GCC organizations where data volumes are growing rapidly.

2. Python for Data Analysis — Python has become the dominant programming language for data analytics in the GCC, surpassing R in market demand. Core proficiency in pandas, NumPy, and matplotlib/seaborn is expected. Experience with scikit-learn for predictive analytics, statsmodels for statistical testing, and automation libraries (requests, BeautifulSoup, Selenium) for data collection adds significant value. GCC employers increasingly expect data analysts to build automated data pipelines and scripts, not just perform ad-hoc analysis.

3. Data Visualization (Power BI & Tableau) — Power BI is the dominant visualization and business intelligence tool across the GCC, driven by widespread Microsoft enterprise adoption in the region. Tableau is common in technology companies and consulting firms. Data analysts must demonstrate the ability to build interactive dashboards, design intuitive visualizations for non-technical stakeholders, implement row-level security, and connect to diverse data sources. Experience building executive dashboards for C-suite audiences is particularly valued in GCC corporate environments where data literacy varies widely among decision-makers.

4. Excel & Google Sheets (Advanced) — Despite the rise of Python and BI tools, advanced spreadsheet skills remain essential for GCC data analysts. Many organizations, particularly in real estate, construction, trading, and government, still rely heavily on Excel for operational data management. Advanced skills including pivot tables, VLOOKUP/INDEX-MATCH, Power Query, Power Pivot, VBA macros, and complex financial modelling are expected. Google Sheets with Apps Script is growing in GCC tech companies. Never underestimate Excel proficiency on your resume — it is often a hard filter in GCC recruitment.

5. Statistical Analysis & Hypothesis Testing — Understanding statistical fundamentals — descriptive statistics, probability distributions, hypothesis testing (t-tests, chi-square, ANOVA), regression analysis (linear, logistic), and correlation analysis — separates data analysts from data entry specialists. GCC employers in banking, insurance, telecommunications, and government increasingly require analysts who can design experiments, validate findings with statistical rigour, and communicate uncertainty and confidence levels to business stakeholders.

6. ETL & Data Pipeline Development — As GCC organizations move toward modern data architectures, data analysts who can build and maintain ETL (Extract, Transform, Load) processes are in high demand. Experience with tools like Apache Airflow, dbt (data build tool), Azure Data Factory, or AWS Glue demonstrates the ability to automate data workflows. Even for analysts who are not full data engineers, understanding how data flows from source systems through transformation to analytical layers is critical for working effectively in GCC enterprise environments.

7. Cloud Platform Proficiency (Azure/AWS/GCP) — Cloud adoption is accelerating rapidly across the GCC. The UAE and Saudi Arabia host major cloud regions for AWS, Azure, and GCP, with data residency requirements often mandating that data remain within the Gulf. Data analysts should demonstrate proficiency in at least one cloud platform — Azure is the most common in GCC enterprises (Azure Synapse, Azure Data Factory, Azure Databricks), while AWS and GCP are prevalent in technology companies and startups. Cloud certification validates your ability to work in the infrastructure environments that GCC employers are investing in.

8. Business Intelligence & Reporting — Beyond creating dashboards, data analysts must understand how to design KPI frameworks, build reporting cadences, and create self-service analytics environments. GCC organizations often need analysts who can define metrics that align with strategic objectives (Vision 2030 KPIs, Emiratisation targets, ESG reporting requirements) and build automated reporting that serves multiple stakeholder levels from operational teams to board-level executives. Experience with SSRS (SQL Server Reporting Services) is also valued in the GCC's Microsoft-heavy enterprise landscape.

9. Data Governance & Quality Management — As GCC countries implement data protection laws (UAE PDPL, Saudi PDPL, Bahrain PDPA), data governance is becoming a critical competency. Data analysts should understand data classification, personally identifiable information (PII) handling, data quality validation, master data management, and audit trail requirements. For roles in banking, healthcare, and government, demonstrating knowledge of regulatory compliance frameworks and data governance best practices is increasingly a requirement rather than a differentiator.

10. Domain Knowledge (Finance/Energy/Real Estate) — GCC data analysts who combine technical skills with deep domain knowledge in key regional industries command significant salary premiums. Oil and gas analytics (production optimization, asset management, predictive maintenance) is highly valued at Aramco, ADNOC, and QatarEnergy. Financial analytics for Islamic banking, wealth management, and regulatory reporting is in demand at Emirates NBD, Al Rajhi Bank, and FAB. Real estate analytics (property valuation models, market demand forecasting, occupancy optimization) is relevant across the UAE's massive property sector. Develop and highlight domain expertise relevant to the GCC industries you are targeting.

Professional Summary Examples

Entry-Level Data Analyst

Detail-oriented Data Analyst with 2 years of experience in data analysis and visualization, recently completed a Master's in Data Science from the University of Birmingham, Dubai campus. Built and maintained 15+ Power BI dashboards tracking operational KPIs for a Dubai-based logistics company, reducing manual reporting effort by 30 hours per week. Proficient in Python (pandas, matplotlib, scikit-learn), SQL (PostgreSQL, BigQuery), and advanced Excel with experience cleaning and analyzing datasets exceeding 5 million rows. Google Data Analytics and Microsoft Power BI Data Analyst Associate certified. Seeking a data analyst position to drive data-driven decision-making in a GCC organization undergoing digital transformation.

Mid-Career Data Analyst

Results-driven Data Analyst with 6 years of experience in the GCC, specializing in financial analytics and business intelligence for the banking and financial services sector. Currently leading analytics for the retail banking division at Emirates NBD, building predictive models and dashboards that support AED 12 billion in annual lending decisions. Designed a customer churn prediction model using Python and machine learning that identified at-risk segments with 87% accuracy, enabling targeted retention campaigns that saved an estimated AED 45M in annual customer lifetime value. Expert in Power BI, Python, SQL, and Azure cloud analytics with strong understanding of UAE data protection regulations and Central Bank reporting requirements.

Senior Data Analyst

Senior Data Analyst with 10 years of experience including 7 years in the GCC, leading analytics strategy and execution for large-scale operations in oil and gas, real estate, and government sectors. At ADNOC, led a team of 4 analysts delivering operational analytics that optimized production scheduling, reducing downtime by 12% and generating an estimated $18M in annual value. Built the enterprise analytics framework using Azure Synapse, Power BI, and Python that serves 200+ internal users across 8 business units. Certified in Azure Data Engineer Associate and Tableau Desktop Specialist with a track record of transforming raw data into strategic insights that drive executive decision-making. Published speaker at Data and AI Summit Dubai on predictive analytics in the energy sector.

Work Experience Examples

• Developed an automated financial reporting pipeline using Python (pandas, openpyxl) and SQL that consolidated data from 6 source systems into a unified Power BI dashboard, reducing the monthly close reporting cycle from 5 days to 8 hours and eliminating 95% of manual data entry errors for the finance team.
• Built a customer segmentation model using Python (scikit-learn, K-means clustering) on a dataset of 2.3 million customer transactions, identifying 5 distinct customer profiles that informed a targeted marketing campaign generating AED 8.5M in incremental revenue over 6 months.
• Designed and deployed 25+ interactive Power BI dashboards for C-suite executives and department heads across the organization, implementing row-level security, automated data refresh, and mobile-optimized layouts that increased dashboard adoption from 15% to 78% across 500+ users.
• Conducted a comprehensive analysis of employee attrition patterns using 5 years of HR data (6,000+ records), identifying key turnover drivers and building a logistic regression model with 82% accuracy that enabled proactive retention interventions, reducing voluntary turnover by 14% year-over-year.
• Automated weekly and monthly operational reports using Python scripts deployed on Azure Functions, replacing a manual process that required 3 team members spending a combined 40 hours per week on data collection and spreadsheet preparation across 4 departments.
• Performed statistical A/B testing analysis for the e-commerce platform's pricing experiments using Python (scipy, statsmodels), analyzing 180,000+ user sessions to identify optimal pricing strategies that increased average order value by 11% and conversion rate by 6.3%, generating an estimated AED 15M in additional annual revenue.
• Created a real estate market intelligence dashboard integrating data from the Dubai Land Department (DLD), Property Finder, and internal sales data, providing real-time analysis of transaction volumes, price trends, and competitive positioning across 8 Dubai communities, used daily by the sales and investment teams to inform pricing decisions.
• Led data quality improvement initiative, implementing automated validation rules and anomaly detection using Python and Great Expectations that reduced data quality issues by 72%, establishing a data governance framework that became the standard across the organization's 12 reporting systems.
• Developed a predictive maintenance model for a fleet of 500+ industrial assets using time-series analysis and random forest classification, achieving 85% accuracy in predicting equipment failures 14 days in advance, reducing unplanned downtime by 23% and saving an estimated AED 6M in annual maintenance costs.
• Partnered with the strategy team to build a comprehensive KPI tracking system aligned with Saudi Vision 2030 objectives, collecting and analyzing data from 15 government departments, producing quarterly performance reports presented to the board and used for strategic planning and resource allocation decisions.

Education & Certifications

GCC employers for data analyst roles typically require a Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, Economics, or a related quantitative field. A Master's degree in Data Science, Analytics, or a related field is increasingly preferred for mid-to-senior roles and can substitute for years of experience. Professional certifications validate specific technical competencies and are highly valued in the GCC, where data analytics is a relatively new professional discipline and certifications serve as trusted quality signals.

Recommended Certifications:

• Microsoft Certified: Power BI Data Analyst Associate (PL-300) — The most directly relevant certification for data analysts in the GCC market. Power BI dominance in the region means this certification is frequently listed as a requirement or strong preference in job descriptions. It validates your ability to design, build, and deploy Power BI solutions.
• Google Data Analytics Professional Certificate — A comprehensive foundational certification covering the entire data analysis workflow. Well-recognized in the GCC and particularly valued for entry-to-mid-level positions. Demonstrates structured analytical thinking and proficiency in R, SQL, Tableau, and spreadsheets.
• AWS Certified Data Analytics - Specialty — Validates expertise in AWS analytics services including Kinesis, Athena, Redshift, and QuickSight. Valuable for data analysts working in cloud-native environments, which is increasingly common in GCC technology companies and startups.
• Tableau Desktop Specialist / Certified Data Analyst — Relevant for organizations using Tableau, common in consulting firms and technology companies in the GCC. The Certified Data Analyst credential validates advanced analytical and visualization skills.
• Azure Data Fundamentals (DP-900) / Azure Data Engineer Associate (DP-203) — Azure certifications are particularly valuable in the GCC given Microsoft's dominance in enterprise IT. The DP-203 is for analysts who work with data pipelines and engineering tasks alongside their analytical duties.

ATS Optimization Tips for Technology

• List programming languages and tools explicitly: Write out "Python", "SQL", "R", "Power BI", "Tableau", "Excel", "PostgreSQL", "BigQuery", "Snowflake" as separate, clearly identifiable keywords. ATS systems search for exact tool and language names — abbreviations and bundled references may be missed.
• Include specific library and framework names: Terms like "pandas", "NumPy", "scikit-learn", "matplotlib", "seaborn", "TensorFlow", "Apache Airflow", and "dbt" are used as keywords by technical recruiters in the GCC. List them in your skills section and reference them naturally in your work experience bullets.
• Use industry-standard job title variations: Include terms like "Data Analyst", "Business Intelligence Analyst", "BI Analyst", "Analytics Analyst", and "Data Analytics" to capture different search variations used by GCC recruiters. This is especially important as job titles are less standardized in the GCC than in mature markets.
• Quantify data volumes and business impact: Include terms like "5 million rows", "2TB dataset", "AED 50M revenue impact", "200+ users" — these contextual numbers help ATS and human reviewers assess the scale of your experience quickly.
• Reference cloud platforms with certification names: "Azure Synapse Analytics", "AWS Redshift", "Google BigQuery", "Azure Data Factory" — cloud keywords are increasingly important as GCC organizations migrate to cloud analytics. Including certification names ("PL-300", "DP-203") also helps match against technical screening criteria.
• Include methodology keywords: Terms like "ETL", "data pipeline", "data governance", "data quality", "A/B testing", "statistical analysis", "predictive modeling", "machine learning" are high-value keywords that position you as a modern data analyst rather than a traditional reporting specialist.

Common Resume Mistakes for Data Analyst

1. Listing tools without demonstrating business impact: Stating "Proficient in Python, SQL, and Power BI" without connecting these skills to business outcomes is the most common mistake for data analyst resumes. GCC employers hire analysts to solve business problems, not to operate software. Every technical skill mentioned should be tied to a tangible result: "Used Python to build a customer segmentation model that identified AED 8.5M in untapped revenue" demonstrates far more value than simply listing Python as a skill.

2. Presenting yourself as a reporting specialist rather than an analyst: Many data analyst candidates describe their role as creating reports and dashboards — this positions you as an operator, not an analyst. Reports are a delivery mechanism, not the analysis itself. Emphasize the insights you discovered, the recommendations you made, the decisions you influenced, and the business outcomes that resulted. "Built weekly sales dashboard" is less compelling than "Identified a 15% revenue leakage through sales data analysis, built an executive dashboard to track corrective actions, and contributed to AED 4M in recovered revenue over 6 months."

3. Not demonstrating scale and complexity: GCC enterprises — particularly in oil and gas, banking, telecommunications, and government — operate at significant data scale. If you have experience working with large datasets (millions of rows, terabytes of data), complex multi-source integrations, or real-time analytics, quantify this clearly. Vague descriptions like "analyzed data" do not convey whether you worked with a 500-row spreadsheet or a 500-million-row data warehouse. Scale matters enormously to GCC employers evaluating candidate readiness.

4. Omitting domain knowledge for key GCC industries: A technically strong resume that does not reference any industry-specific analytics experience misses the opportunity to differentiate. If you have analytics experience in oil and gas, banking, real estate, government, aviation, or e-commerce — all major GCC sectors — highlight the domain-specific problems you solved and the industry metrics you tracked. GCC employers value analysts who understand their business, not just the tools.

5. Ignoring soft skills and stakeholder management: Data analysis is ultimately about influencing decisions. GCC employers consistently report that data analysts who can communicate findings clearly to non-technical stakeholders, present to senior leadership, and translate business questions into analytical frameworks are far more valuable than those who only produce technical outputs. If you have experience presenting to C-suite executives, training business users on dashboards, or collaborating with cross-functional teams, highlight these experiences prominently.

GCC Market Insights for Data Analyst

The demand for data analysts in the GCC has surged dramatically over the past three years, driven by comprehensive national digital transformation agendas and the explosive growth of data-intensive industries including fintech, e-commerce, smart cities, and renewable energy. The Saudi Data and AI Authority (SDAIA) estimates that the Kingdom alone needs to develop or attract 20,000+ data and AI professionals by 2030 to meet Vision 2030 targets. The UAE's AI Strategy 2031 similarly positions data talent as critical national infrastructure. This macro-level demand translates into robust hiring activity and competitive compensation for skilled data analysts.

Salary Trends: Data analyst salaries in the GCC are competitive and rising. In the UAE, mid-level data analysts (3-6 years experience) earn between AED 15,000 and AED 30,000 per month, with senior data analysts and analytics managers commanding AED 30,000-50,000+. Saudi Arabia offers comparable salaries, particularly in Riyadh's growing tech sector: SAR 12,000-25,000 for mid-level and SAR 25,000-45,000+ for senior roles. Salaries are highest in the financial services, oil and gas, and technology sectors. Data analysts with Python proficiency earn 20-30% more than those relying solely on Excel and BI tools. Cloud certification (Azure, AWS) adds another 10-15% salary premium. Packages include housing allowance, annual flights, medical insurance, and in some cases, performance bonuses tied to project outcomes.

Top Hiring Companies: The GCC data analytics job market is anchored by several categories of employers. Technology and digital platforms: Careem, Noon, Talabat, Kitopi, and a growing ecosystem of GCC-based startups. Oil and gas majors: Saudi Aramco, ADNOC, QatarEnergy, and Kuwait Petroleum Corporation, all of which have massive data analytics programs. Financial services: Emirates NBD, First Abu Dhabi Bank (FAB), Al Rajhi Bank, Saudi National Bank, and fintech companies like Tabby, Tamara, and STC Pay. Consulting firms: McKinsey, BCG, Deloitte, PwC, and EY all have significant data analytics practices in the GCC. Government entities: Dubai Digital Authority, SDAIA, Abu Dhabi Digital Authority, and smart city initiatives across the region. Telecommunications: Etisalat (e&), du, STC, and Zain also employ large analytics teams.

In-Demand Specializations: Financial analytics (regulatory reporting, credit risk, fraud detection) and energy sector analytics (production optimization, ESG reporting, predictive maintenance) command the highest premiums. E-commerce and customer analytics are growing rapidly with the expansion of platforms like Noon, Amazon.ae, and Namshi. Government and public sector analytics — particularly for smart city initiatives, healthcare optimization, and education performance — represent a significant and growing segment of the market. Real-time analytics, machine learning-augmented analysis, and cloud-native data solutions are the fastest-growing technical specializations. Data analysts who can bridge the gap between pure analytics and machine learning engineering are exceptionally well-positioned.

Visa & Work Permits: Data analysts and technology professionals benefit from favorable visa conditions across the GCC. The UAE's Golden Visa program includes technology professionals, and specialized tech visas have been introduced to attract digital talent. Saudi Arabia's Premium Residency and the growing tech hub in Riyadh provide attractive options. Many GCC technology companies offer comprehensive relocation packages including visa processing, flights, temporary accommodation, and family sponsorship. The GCC's zero income tax environment means that data analyst salaries translate to significantly higher take-home pay compared to equivalent roles in Europe, North America, or Australia — a major attraction for international data talent.

Data Analyst Resume Sample — GCC Optimized

Priya Venkatesh

Dubai, UAE | +971-56-XXX-XXXX | [email protected] | linkedin.com/in/priyavenkatesh | github.com/priyavenkatesh

Nationality: Indian | Visa Status: UAE Residence Visa (Employer-Sponsored) | Languages: English (Fluent), Hindi (Native), Tamil (Native), Arabic (Basic)

Professional Summary

Data Analyst with 6 years of experience in business intelligence, statistical analysis, and data visualization, including 4 years in the GCC working across financial services and e-commerce sectors. Currently leading analytics for the digital banking division at a top-tier UAE bank, building predictive models and executive dashboards that support AED 8 billion in annual digital transaction volume. Designed and deployed a fraud detection analytics framework using Python and machine learning that identified AED 12M in suspicious transactions in its first year. Microsoft PL-300 and Google Data Analytics certified, proficient in Python, SQL, Power BI, Tableau, and Azure cloud analytics. Passionate about transforming complex data into clear, actionable business insights for stakeholders at all levels.

Key Achievements

• Fraud Detection Framework — Built a real-time fraud scoring model using Python (scikit-learn, XGBoost) integrated with the bank's transaction monitoring system, flagging AED 12M in suspicious transactions during the first 12 months of deployment with a false positive rate of only 3.2%, enabling the compliance team to focus investigations on high-confidence alerts.
• Executive Analytics Platform — Designed and launched the digital banking division's first self-service Power BI analytics platform, consolidating data from 8 source systems into a unified data model serving 45 dashboards used by 120+ stakeholders from branch managers to the CEO, reducing ad-hoc reporting requests by 65%.
• Customer Churn Prediction — Developed a logistic regression model predicting customer churn with 84% accuracy, identifying 15,000 at-risk accounts that were targeted with personalized retention offers, resulting in a 22% reduction in quarterly churn rate and an estimated AED 28M in preserved customer lifetime value.

Technical Skills

Programming & Query: Python (pandas, NumPy, scikit-learn, matplotlib, seaborn, statsmodels, XGBoost), SQL (PostgreSQL, Microsoft SQL Server, BigQuery), R (basic), VBA
Visualization & BI: Power BI (DAX, Power Query, row-level security, paginated reports), Tableau Desktop & Server, Looker, Google Data Studio
Cloud & Data Engineering: Azure (Synapse Analytics, Data Factory, Databricks, Blob Storage), AWS (S3, Redshift, Athena), Snowflake
Databases: PostgreSQL, Microsoft SQL Server, MySQL, MongoDB (basic), Google BigQuery
Other Tools: Excel (advanced: pivot tables, Power Query, Power Pivot, VBA macros), Jupyter Notebooks, Git/GitHub, Jira, Confluence, Apache Airflow (basic), dbt
Methods: Statistical analysis, hypothesis testing, A/B testing, regression analysis, clustering, classification, time-series analysis, ETL development, data quality assurance, data governance

Work Experience

Senior Data Analyst — Emirates NBD (Dubai, UAE) | Apr 2023 – Present

• Lead analytics for the digital banking division, supporting AED 8 billion in annual digital transaction volume across mobile banking, online banking, and digital payments channels, reporting to the Head of Digital Strategy.
• Build and maintain 45+ Power BI dashboards with automated data refresh from Azure Synapse, implementing row-level security across 5 user groups and serving 120+ daily active users across the division.
• Developed the fraud detection scoring model using Python and XGBoost, integrating with the bank's real-time transaction monitoring system via Azure Functions, flagging AED 12M in suspicious activity with 96.8% precision in the first year.
• Conduct monthly A/B testing analysis for digital product features using Python (scipy, statsmodels), analyzing 500,000+ user sessions per test to measure impact on conversion rates, user engagement, and transaction volumes, directly informing the product roadmap.
• Partner with the Central Bank reporting team to automate regulatory data submissions, building Python-based ETL pipelines that reduced quarterly reporting preparation time from 15 person-days to 2 person-days while improving data accuracy to 99.7%.

Data Analyst — Noon (Dubai, UAE) | Jan 2021 – Mar 2023

• Analyzed e-commerce performance data across 20+ product categories covering 10 million+ SKUs, building category-level dashboards and weekly business review reports for the commercial leadership team.
• Designed customer segmentation model using K-means clustering on purchase behaviour data for 3.5 million active customers, enabling personalized marketing campaigns that increased repeat purchase rate by 18% and average order value by 9%.
• Built automated pricing intelligence pipeline using Python (requests, BeautifulSoup, pandas) that tracked competitor pricing across 50,000 key SKUs daily, providing the pricing team with real-time competitive benchmarks that informed dynamic pricing decisions.
• Reduced data pipeline failures by 40% by implementing automated data quality checks using Great Expectations and creating anomaly detection alerts that notified the data engineering team of source system issues within 15 minutes.

Junior Data Analyst — Tata Consultancy Services (Mumbai, India) | Jul 2019 – Dec 2020

• Supported business intelligence reporting for a major Indian banking client, maintaining 20+ Tableau dashboards tracking loan portfolio performance, credit risk metrics, and branch-level KPIs for 500+ branches nationwide.
• Wrote complex SQL queries across Oracle and PostgreSQL databases to extract, transform, and analyze transaction data for regulatory compliance reporting, processing 50 million+ daily transactions.
• Automated 12 recurring Excel-based reports using Python (pandas, openpyxl, schedule), saving 25 hours of manual effort per week across the analytics team and eliminating copy-paste errors.

Education

Master of Science in Data Science — University of Birmingham, Dubai Campus (2022, part-time while working)
Bachelor of Engineering in Computer Science — University of Mumbai, India (2019)
All degrees attested by UAE Ministry of Education for employment purposes

Certifications

• Microsoft Certified: Power BI Data Analyst Associate (PL-300) — Current
• Google Data Analytics Professional Certificate — 2021
• Azure Data Fundamentals (DP-900) — 2023
• Tableau Desktop Specialist — 2022
• IBM Data Science Professional Certificate — 2020

Portfolio & Community

• GitHub: 15+ public repositories including GCC real estate price prediction model, Arabic sentiment analysis toolkit, and automated financial reporting framework
• Kaggle: Competitions Expert rank with 3 bronze medals in tabular data competitions
• Speaker: "Data Analytics in GCC Banking" at Data Science Dubai Meetup (2024)