At a Glance
- Tasks: Design and optimise scalable data pipelines on AWS for data-driven decision making.
- Company: Join a forward-thinking tech company focused on data and AI innovation.
- Benefits: Flexible work options, continuous learning, and a commitment to diversity and inclusion.
- Other info: Exciting opportunities for growth in a supportive and inclusive environment.
- Why this job: Make an impact in cloud technologies while collaborating with talented professionals.
- Qualifications: Experience in data engineering, AWS services, and strong analytical skills.
The predicted salary is between 50000 - 70000 € per year.
The team you'll be working with: We are seeking a detail-oriented and capable Data Engineer – AWS to join our Data & AI practice. The successful candidate will bring solid experience in data engineering, ETL/ELT pipeline development, and cloud-native platforms, with a strong focus on AWS-based data ecosystems. This role is key to supporting the design, development, and optimisation of scalable data pipelines and data platforms, enabling data-driven decision making, analytics, and downstream applications. You will contribute to ensuring data quality, reliability, and performance, through structured engineering and testing practices.
You will work closely with architects, engineers, and analysts to deliver secure, high-performance data solutions, using technologies such as AWS Glue, Python/PySpark, SQL, and configuration-driven frameworks (e.g., YAML). You should be comfortable working in a collaborative, delivery-focused environment and have a strong interest in cloud technologies, modern data platforms, and software engineering best practices.
Responsibilities
- Support delivery across data engineering and platform development initiatives
- Collaborate with architects, engineers, and stakeholders to implement data solutions on AWS
- Assist in planning and executing engineering tasks, releases, and deliverables
- Build and maintain data pipelines and workflows on AWS platforms
- Develop ETL/ELT pipelines using AWS Glue, Python / PySpark, SQL, Configuration-driven frameworks (e.g., YAML)
- Support ingestion, transformation, and processing of structured and semi-structured data
- Contribute to the development of scalable, reusable data components and services
- Test and validate data pipelines and processing jobs running on AWS services
- Develop and execute data validation and reconciliation queries using SQL
- Work with AWS services including: AWS Glue, S3-based data lakes, related data processing and orchestration services
- Support implementation of modern data platforms, including data lakes and lakehouse-style architectures
- Optimise data jobs for performance, scalability, and cost efficiency
Experience & Qualifications
- Experience in data engineering or software engineering with a data focus
- Strong interest in cloud-based data platforms and distributed processing
- Good analytical and problem-solving skills
- Attention to detail and commitment to data quality and reliability
- Effective communication and teamwork skills
- Willingness to learn and develop in AWS and modern data engineering practices
Technical Expertise
- Hands-on experience with AWS cloud services, especially AWS Glue
- Python / PySpark
- SQL querying and data manipulation
- Exposure to YAML or configuration-driven pipelines (desirable)
- Experience building or supporting data pipelines and ETL/ELT processes
- Familiarity with data lakes and/or Lakehouse concepts
- Distributed processing frameworks (e.g., Spark)
- Basic understanding of data architecture patterns and cloud-native development
- Exposure to version control (e.g., Git) and CI/CD pipelines desirable
Benefits
We offer a range of tailored benefits that support your physical, emotional, and financial wellbeing. Our Learning and Development team ensure continuous growth and development opportunities. We also offer the opportunity to have flexible work options.
We are an equal opportunities employer. We believe in the fair treatment of all our employees and commit to promoting equity and diversity in our employment practices. We are also a proud Disability Confident Committed Employer – we are committed to creating a diverse and inclusive workforce. We actively collaborate with individuals who have disabilities and long-term health conditions which have an effect on their ability to do normal daily activities, ensuring that barriers are eliminated when it comes to employment opportunities. In line with our commitment, we guarantee an interview to applicants who declare to us, during the application process, that they have a disability and meet the minimum requirements for the role. If you require any reasonable adjustments during the recruitment process, please let us know. Join us in building a truly diverse and empowered team.
AWS Data Engineer in London employer: NTT America, Inc.
As an AWS Data Engineer at our company, you will thrive in a collaborative and innovative environment that prioritises your professional growth and wellbeing. We offer tailored benefits, flexible work options, and a strong commitment to diversity and inclusion, ensuring that every employee feels valued and empowered. Join us to work with cutting-edge technologies while contributing to impactful data solutions that drive meaningful decision-making.
StudySmarter Expert Advice🤫
We think this is how you could land AWS Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those involving AWS Glue, Python, and SQL. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering questions and AWS services. Practice explaining your past projects and how you tackled challenges. Confidence is key, so get comfortable talking about your experience!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive and engaged with our company.
We think you need these skills to ace AWS Data Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with AWS, data engineering, and ETL/ELT pipelines. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or technologies you've worked with!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how you can contribute to our team. We love seeing enthusiasm for cloud technologies and modern data platforms.
Showcase Your Problem-Solving Skills:In your application, mention specific challenges you've faced in data engineering and how you tackled them. We appreciate candidates who can think critically and demonstrate their analytical skills, especially in a collaborative environment.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our awesome team!
How to prepare for a job interview at NTT America, Inc.
✨Know Your AWS Stuff
Make sure you brush up on your AWS knowledge, especially around services like AWS Glue and S3. Be ready to discuss how you've used these tools in past projects, as well as any challenges you faced and how you overcame them.
✨Show Off Your Data Pipeline Skills
Prepare to talk about your experience with ETL/ELT processes. Have specific examples ready that showcase your ability to build and maintain data pipelines, and be prepared to explain the technologies you used, like Python or SQL.
✨Collaboration is Key
Since this role involves working closely with architects and engineers, think of examples where you've successfully collaborated in a team setting. Highlight your communication skills and how you contribute to a positive team dynamic.
✨Be Ready for Problem-Solving Questions
Expect some technical questions that test your analytical and problem-solving skills. Practice explaining your thought process when tackling data quality issues or optimising data jobs for performance and cost efficiency.
