Data Analyst

Job Profile Summary:

A data analyst is responsible for collecting, cleaning, analyzing, and visualizing data to help organizations make informed decisions.

They use statistical techniques to identify trends and patterns in data and communicate their findings through reports and visualizations.

To be successful in this role, data analysts need to have strong analytical skills, attention to detail, and the ability to communicate complex ideas in a simple way.

They should also have a strong understanding of statistics and data analysis techniques, as well as proficiency in programming languages and data visualization tools.

Job Description:

Responsible for collecting, analysing and interpreting large datasets to help drive informed business decisions and improve overall performance.

Utilizing methodologies and approaches such as Data collection by gathering relevant data from various internal and external sources, Data Cleaning and Preparation: Processing raw data to ensure data quality.

Data Analysis: Applying statistical and analytical techniques to extract meaningful insights from the data.

Data Visualization: Creating charts, graphs, and other visual representations to communicate findings effectively through clear storytelling and also advises users appropriately on this.

Reporting and Presentation: Preparing reports and presentations to share insights with stakeholders.

Tool and Technology Management: Utilizing and maintaining relevant software and tools for data analysis and visualization.

Statistics and probability: be familiar with basic statistical concepts such as hypothesis testing, Monte Carlo simulations, expected values, standard errors, Central Limit Theorem, confidence intervals, etc.

This is key to be able to draw accurate conclusions from data and avoid biases and wrong decision making

Ensuring data security, privacy, and compliance with internal requirements and external regulations.

Working closely with other teams and departments to understand their data needs and provide actionable insights.

Be the interface between their functions, the data ecosystem and the requested solution.

Coach the function in the data-driven mind-set.

Actively contribute and share best practices, findings, and techniques with the wider data community.

Key Competencies: Competency and Skill Level

Applied probabilities & Statistics = 2 - Autonomous Level

Data Governance Management System = 2 - Autonomous Level

Data Science: Advanced Analytics = 2 - Autonomous Level

Data Science: Data visualisation & Coms = 3 - Advanced Level

Data Science: Data Wrangling = 2 - Autonomous Level

Data Security = 1 - Basic Level

Generative AI Essentials & Prompting = 3 - Advanced Level

Eco-design & Sustainability of Digital services = 1 - Basic Level

Competency Scale:

Basic Level: Basic level of expertise, performs routine and/or recurrent tasks, implies partial supervision.

Autonomous Level : Ability to solve problems autonomously, no supervision required in these tasks. Can deal with unforeseen issues.

Advanced Level: High Level of knowledge and wide experience which is internally recognised. Could be a mentor/coach/advisor to support skill development of other colleagues

Education and Experience:

Education: Bachelor’s degree in a quantitative technical field (e.g., Data Science, Statistics, Mathematics, or Aerospace Engineering). A Master’s or Ph.D. in a quantitative field (e.g., Physics, Mathematics, Aerospace Engineering, or Computer Science) is an advantage.

Industry Tenure: 3+ years of professional experience in a data-centric role.

Subject matter expertise in business or product data, with a strong preference for candidates familiar with aviation-specific datasets (flight telemetry, maintenance logs, or passenger data).

Expert proficiency in SQL and querying relational databases (PostgreSQL, MySQL, Oracle).

Strong command of Python or R for statistical modeling and Excel for rapid data manipulation.

Proven experience building high-impact dashboards in Tableau, Power BI, or similar platforms.

Experience with terminal-based AI tools (e.g., Claude Code) and a working knowledge of agentic frameworks like Google ADK.

Solid understanding of statistical concepts (hypothesis testing, regressions, distributions) and data cleaning techniques for complex, messy datasets.

Demonstrated ability to work within a Quality Management System (QMS). You must ensure all analysis is reproducible, documented, and ready for industry audits.

High degree of accuracy when handling complex data structures to ensure "aircraft-grade" reliability in reporting.

Ability to translate raw numbers into "The Story Behind the Data," providing actionable recommendations to non-technical leaders and global stakeholders.

A collaborative mindset, ready to partner with AI Engineers and Product Managers to ensure data insights drive end-to-end product success.

A self-starter who keeps pace with emerging data technologies and shifts within the aviation industry.

This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.