At a Glance
- Tasks: Build scalable data pipelines and collaborate on innovative AWS data solutions.
- Company: Join a forward-thinking tech company committed to diversity and inclusion.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a strong emphasis on learning and development.
- Why this job: Make an impact in the cloud data space while developing your skills in AWS.
- Qualifications: Experience in data engineering or software engineering with a focus on data.
The predicted salary is between 50000 - 60000 £ per year.
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.
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, using YAML configuration.
- 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 and S3‑based data lakes.
- Support implementation of modern data platforms, including data lakes and lakehouse‑style architectures.
- Optimize 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.
Technical Practices
- Experience with AWS cloud services, especially AWS Glue, Python/PySpark, and SQL querying.
- Exposure to YAML or configuration‑driven pipelines.
- Experience building or supporting data processes.
- Familiarity with data lakes and/or lakehouse concepts.
- Experience with distributed processing frameworks (e.g., Spark).
- Basic understanding of data architecture and architecture principles.
- Exposure to version control (Git) and CI/CD pipelines.
NTT DATA is an equal opportunities employer. We commit to promoting equity and diversity in our employment practices and providing reasonable adjustments for applicants with disabilities.
AWS Data Engineer: Build Scalable Data Pipelines employer: NTT DATA
At NTT DATA, we pride ourselves on being an excellent employer, offering a dynamic work culture that fosters collaboration and innovation within our Data & AI practice. Our commitment to employee growth is evident through continuous learning opportunities in cutting-edge technologies like AWS, while our focus on equity and diversity ensures a supportive environment for all. Join us in a location that champions modern data engineering, where your contributions will directly impact the development of scalable data solutions.
StudySmarter Expert Advice🤫
We think this is how you could land AWS Data Engineer: Build Scalable Data Pipelines
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues 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 pipelines and projects you've worked on, especially those using AWS services. This gives you a tangible way to demonstrate your expertise during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on your SQL and AWS knowledge. Practice common data engineering problems and be ready to discuss your thought process. We want to see how you tackle challenges!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at StudySmarter.
We think you need these skills to ace AWS Data Engineer: Build Scalable Data Pipelines
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with AWS and data engineering. We want to see how your skills align with the job description, 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 and a bit of personality in your application.
Showcase Your Technical Skills:Don’t forget to mention your experience with AWS services like Glue and S3, as well as your proficiency in SQL and Python/PySpark. We’re looking for candidates who can hit the ground running, so highlight any relevant technical skills!
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’s super easy – just follow the prompts!
How to prepare for a job interview at NTT DATA
✨Know Your AWS Inside Out
Make sure you brush up on your knowledge of AWS services, especially 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.
✨Showcase Your Data Pipeline Skills
Prepare to talk about your experience with ETL/ELT pipeline development. Have specific examples ready that demonstrate your ability to build and maintain data pipelines, and be prepared to explain the YAML configuration process.
✨Demonstrate Problem-Solving Abilities
Expect questions that test your analytical skills. Think of scenarios where you had to troubleshoot data issues or optimise performance. Highlight your approach to problem-solving and how you ensure data quality and reliability.
✨Communicate Effectively
Since collaboration is key in this role, practice articulating your thoughts clearly. Be ready to discuss how you've worked with architects and engineers in the past, and how you can contribute to a team environment.
