Data Engineering Intern

Thales is a global technology leader trusted by governments institutions and enterprises to tackle their most demanding challenges. From quantum applications and artificial intelligence to cybersecurity and 6G innovation our solutions empower critical decisions rooted in human intelligence. Operating at the forefront of aerospace and space cybersecurity and digital identity were driven by a mission to build a future we can all trust.

Key Responsibilities:

These roles and responsibilities outline the key tasks and expectations for the data engineering internship role within the AIR Lab focusing on data engineering analysis and potential applications of AI/ML techniques to aviation innovation and research.

1. Understanding of Current Data Infrastructure
Familiarisation with existing data lake architecture and data engineering stack used within the AIR Lab. Gain insights into data storage processing frameworks and integration points.

2. Data Feed Understanding
Gain a comprehensive understanding of data feeds relevant to aviation innovation and research. Identify sources formats and frequency of data feeds. Ensure the reliability and consistency of data feeds for further analysis.

3. ETL and Data Analysis
Develop and execute Extract Transform Load (ETL) processes to cleanse transform and integrate aviation data. Perform exploratory data analysis to uncover patterns trends and domain inefficiencies in the data. Collaborate with AIR Lab team to understand specific data requirements and provide insights.

4. AI/ML and NLP Applications (time permitting)
Explore opportunities for applying artificial intelligence (AI) and machine learning (ML) techniques to aviation data. Develop and implement AI/ML models for predictive analytics anomaly detection or optimisation tasks. Investigate Natural Language Processing (NLP) applications for extracting aviation-related data and trends.

5. Documentation and Reporting
Document processes methodologies and findings of data engineering and analysis tasks. Create reports and presentations summarising insights trends and recommendations for stakeholders.

6. Collaboration and Communication
Collaborate effectively with team members including researchers data scientists and other engineers. Give feedback and seek guidance to/from your mentor(s) to enhance transparency and project outcomes.

7. Continuous Learning and Skill Development
Stay updated with the latest advancements in data engineering AI/ML and aviation technology. Participate in training sessions workshops or online courses to enhance technical skills and domain knowledge. Proactively explore new tools and techniques to optimise data processing and analysis workflows.

Qualifications & Profile Guidelines:

Educational Background: The candidate should be enrolled in or have completed a masters degree program in a relevant field such as computer science data science engineering or a related discipline.

Strong Analytical Skills: Proficiency in data analysis techniques and tools is essential. Candidates should be able to analyse large datasets identify trends and extract actionable insights.

Programming Skills: Proficiency in languages commonly used in data engineering such as Python R SQL or Java. Experience with data manipulation libraries (e.g. Pandas NumPy) is a plus.

Data-related Knowledge Processing and Transformation: Understanding of database systems and query languages familiarity with relational databases (e.g. MySQL PostgreSQL) and/or NoSQL databases (e.g. ElasticSearch).

Experience with data processing and transformation techniques such as ETL (Extract Transform Load) processes data cleansing and normalisation.

Big Data Technologies: Have familiarity with big data technologies and frameworks such as Spark Kafka Iceberg.

Data Visualisation: Proficiency in data visualisation tools and techniques to communicate insights effectively is desirable. Experience with tools like Kibana Matplotlib will be advantageous.

Problem-Solving Skills: Ability to approach problems creatively and independently with a strong aptitude for problem-solving and troubleshooting.

Team Player: Capability to work collaboratively in a multidisciplinary team environment sharing insights and collaborating with subject matter experts and engineers from diverse backgrounds.

Attention to Detail: Ability to pay close attention to detail is crucial for ensuring the accuracy and reliability of data engineering processes.

Interest in Aviation: An interest in aviation or previous experience in aviation-related projects will be beneficial for understanding domain-specific challenges and requirements.

Benefits:
Opportunities for professional development and training.
Collaborative and innovative work environment.
Chance to work on cutting-edge aerospace projects with a global impact.

We encourage applications from candidates of all backgrounds and experiences.

If you are passionate about aviation data and AI/ML applications to this space and excited to contribute to the safety and security of aerospace solutions please apply with your resume and a cover letter detailing your relevant experience and qualifications.

Join us at AIR Lab and be part of a team that is pushing the boundaries of innovation in the aviation and aerospace industry!

At Thales were committed to fostering a workplace where respect trust collaboration and passion drive everything we do. Here youll feel empowered to bring your best self thrive in a supportive culture and love the work you do. Join us and be part of a team reimagining technology to create solutions that truly make a difference for a safer greener and more inclusive world.