Data Analyst

NEOM is an accelerator of human progress and a vision of what a new future might look like. A region in northwest Saudi Arabia on the Red Sea, NEOM is being built from the ground up to include hyperconnected, cognitive cities, ports, next- infrastructure and industries, enterprise zones, research centers, sports and entertainment venues and tourist destinations.

As a destination, it will be a home for people who dream big and want to be part of building a new model for exceptional livability, creating thriving businesses and reinventing environmental conservation.

As a workplace, it is a place for people who share our core values of care, curiosity, , passion, respect, and becoming a catalyst for change.

Are you ready to help NEOM find solutions to the world’s most pressing challenges? Are you prepared to create a lasting legacy that benefits to come? Then we want to hear from you!

ROLE OVERVIEW

The Data Analyst will contribute to critical projects focused on data collection, integration, and analysis. and will assist in acquiring and preparing datasets to ensure accuracy and reliability, supporting the team in maintaining high standards of data quality and governance.

Also working closely with Data Engineers, Analysts, and other stakeholders, helping to clean and organize data, conduct analyses, and generate insights to inform decision-making. and fostering efficient data integration, advanced analytics, and effective project delivery.

ROLE COMPETENCIES & ACTIVITIES

The Data Analyst's responsibilities will include, but not be limited to:

Data Acquisition, Processing, Engineering and Management

• Assist in the end-to-end process of data acquisition, ensuring data is collected, cleaned, and prepared for analysis according to best practices.
• Support the integration of diverse data sources into centralized systems for analysis and reporting.
• Contribute to the implementation of ETL/ELT pipelines, ensuring data is transformed and loaded efficiently into data warehouses or data lakes.
• Work closely with data engineers to automate data workflows and optimize performance for large-scale data processing.
• Help maintain robust and scalable data management processes to support informed decision-making and department objectives.

Data Analysis

• Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies within datasets.
• Clean, preprocess, and transform data to ensure it is ready for analysis and reporting.
• Conduct statistical analysis to derive insights and support data-driven decision-making.
• Create visualizations and dashboards to communicate findings effectively to stakeholders.
• Collaborate with team members to interpret data and develop actionable recommendations.
• Ensure data analysis aligns with project goals and contributes to achieving department objectives.

Testing and Quality Assurance

• Assist in validating and ensuring the quality of acquired and processed data, verifying compliance with predefined standards.
• Participate in regular reviews of ETL/ELT pipelines to address issues related to data integrity, accuracy, and performance.
• Support the implementation of continuous data quality monitoring and validation processes to ensure reliability across projects.

Planning and Reporting

• Support the planning and execution of data acquisition, processing, and analysis projects, ensuring efficient collection, integration, and storage of data.
• Work collaboratively with internal teams to establish clear objectives, data strategies, and timelines for project delivery.
• Assist in defining data acquisition, processing, and data engineering requirements, contributing to the development of integration standards and deliverables aligned with governance goals.
• Collaborate with data science, analytics, and engineering teams to conduct analyses, implement ETL/ELT workflows, and produce actionable reports.
• Monitor the progress of data projects, track data quality metrics, and report regularly on project status, integrity, and outcomes to stakeholders.

Others

• Identify and suggest solutions to barriers hindering data acquisition, engineering, and processing activities, including inefficiencies and technical challenges.
• Participate in knowledge-sharing activities to foster collaboration and continuous learning within the team.
• Contribute to the improvement of data practices and processes, promoting a culture of transparency and accountability.

Collaboration and Communication

• Assist in presenting data analysis findings in a clear and concise manner to internal teams and stakeholders, ensuring insights are easily understood by non-technical audiences.
• Work collaboratively with team members, including analysts, data engineers, and other departments, to support data-driven decision-making processes.
• Help prepare reports, dashboards, and visualizations to effectively communicate key findings and trends.

Stakeholder Management

• Support data analysis projects by gathering requirements and ensuring stakeholder needs are addressed throughout the project lifecycle.
• Assist in providing regular updates to stakeholders on project progress, data quality, and analysis outcomes.
• Collaborate with internal and external stakeholders to align on project goals, ensuring deliverables meet expectations.

Culture and Values

• Embrace NEOM’s culture and Values https://www.neom.com/en-us/about.
• Act with honesty and integrity by following best practices, and upholding the robust standards and expectations set out in NEOM’s Code of Conduct.
• Maintain fair, ethical and professional work practices in accordance with NEOM’s Values and Code of Conduct.
• Adhere to NEOM’s Policies, procedures, and controls to ensure compliance with rules. 

EXPERIENCE & QUALIFICATIONS

Knowledge, Skills and Experience

• 3+ years of relevent experience
• Experience in data analysis, either through internships, academic projects, or entry-level roles.
• Hands-on experience with data cleaning, processing, and exploratory data analysis.
• Familiarity with statistical concepts and methods applied to real-world problems.
• Proficiency in tools such as Excel, SQL, Python, or R for data analysis and reporting.
• Basic knowledge of data visualization tools like Tableau, Power BI, or similar platforms.
• Familiarity with data pipeline and ETL/ELT workflows is a plus.
   

Qualifications

• Bachelor’s degree in Statistics, Data Science, Computer Science, Economics, or a related field.