At a Glance
- Tasks: Lead the design and optimisation of AWS data pipelines and platforms.
- Company: Join a forward-thinking company focused on data and AI innovation.
- Benefits: Flexible work options, continuous learning opportunities, and a supportive environment.
- Other info: Diverse and inclusive workplace committed to employee wellbeing.
- Why this job: Make a real impact in data engineering while collaborating with talented professionals.
- Qualifications: Experience in data engineering, AWS services, and strong problem-solving skills.
The predicted salary is between 80000 - 100000 € per year.
We are seeking an accomplished and detail-oriented Lead Data Engineer – AWS to join our Data & AI practice. The successful candidate will bring deep expertise in data engineering, distributed data processing, and cloud-native platforms, with a strong focus on AWS-based data ecosystems. This role is critical in designing, building, and optimising end-to-end data pipelines and platforms, enabling scalable data processing, advanced analytics, and AI-driven solutions. You will play a key role in ensuring data quality, integrity, performance, and reliability, supported by strong engineering and testing practices.
As a senior practitioner, you will collaborate with architects, engineers, and analysts to deliver secure, scalable, and high-performing data solutions, leveraging technologies such as AWS Glue, Python/PySpark, SQL, and configuration-driven frameworks (e.g., YAML). You will thrive in a collaborative, client-facing environment, with a passion for solving complex technical challenges, ensuring delivery excellence, and driving modernisation through cloud-native engineering practices.
What you’ll be doing
- Client Engagement & Delivery
- Act as a senior engineer within data engineering and cloud platform initiatives, supporting delivery across complex transformation programmes.
- Collaborate with architects and stakeholders to define and implement scalable AWS-based data solutions.
- Contribute to solution design, estimation, and delivery planning.
- Lead engineering workstreams and ensure high-quality technical delivery.
- Data Engineering & Platform Development
- Design, build, and optimise scalable data pipelines and data processing frameworks on AWS.
- Develop and maintain ETL/ELT pipelines using AWS Glue, Python / PySpark, SQL, and configuration-driven frameworks (e.g., YAML).
- Implement robust data ingestion, transformation, and processing patterns.
- Build reusable data services, components, and frameworks.
- Data Pipeline Testing & Reliability
- Define and implement testing strategies for data pipelines, ensuring reliability and accuracy.
- Validate data processing workflows using Python / PySpark transformations, SQL-based validation logic, and configuration-driven orchestration.
- Develop automated testing, monitoring, and alerting solutions.
- Ensure data completeness, accuracy, consistent transformation behaviour, and drive improvements in observability and pipeline resilience.
- AWS Data Platforms
- Lead development on AWS services including AWS Glue and S3-based data lakes.
- Support implementation of modern data architectures, including data lakes and Lakehouse-style platforms.
- Optimise pipelines and jobs for performance, scalability, and cost efficiency.
- Data Transformation & Modelling
- Define and implement data transformation logic aligned to business requirements.
- Support data modelling approaches for analytics and platform use cases.
- Ensure consistency, usability, and quality across data assets and pipelines.
- Collaboration & Technical Leadership
- Collaborate with Solution Architects, Data Engineers, Analysts, and ML engineers.
- Provide technical leadership and mentoring to engineers within the team.
- Promote engineering best practices, automation, and reusable solutions.
- Contribute to engineering standards, documentation, and knowledge sharing.
- Quality, Governance & Security
- Ensure data quality, integrity, and reliability across data platforms.
- Implement and enforce secure coding and data handling practices.
- Support compliance with GDPR and regulated environment standards (where applicable).
- Contribute to monitoring, auditing, and operational processes.
What experience you'll bring
- Proven experience in data engineering and cloud-based platform delivery.
- Strong understanding of distributed data processing and scalable system design.
- Ability to lead delivery while remaining hands-on technically.
- Strong analytical, problem-solving, and communication skills.
- Experience working in client-facing and delivery-focused environments.
- Ability to mentor and develop engineering teams.
Technical Expertise
- Strong hands-on experience with AWS cloud services, especially AWS Glue.
- Python / PySpark for large-scale data processing.
- SQL for querying, transformation, and validation.
- Configuration-driven development (e.g., YAML).
- Experience building and operating data pipelines, ETL/ELT workflows, and cloud-native data platforms.
- Familiarity with data lakes and Lakehouse concepts, distributed processing frameworks (e.g., Apache Spark).
- Strong understanding of ETL vs ELT patterns, performance tuning, and optimisation.
- Experience with version control (Git), CI/CD, and DevOps practices.
What we’ll offer you
We offer a range of tailored benefits that support your physical, emotional, and financial wellbeing. Our Learning and Development team ensure that there are continuous growth and development opportunities for our people. 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.
Lead AWS Data Engineer in London employer: NTT America, Inc.
As a Lead AWS Data Engineer, you will thrive in a dynamic and inclusive work environment that prioritises employee growth and well-being. Our commitment to continuous learning, flexible work options, and a diverse workforce ensures that you will not only excel in your role but also contribute to meaningful projects that drive innovation. Join us to be part of a collaborative team that values your expertise and fosters a culture of excellence and support.
StudySmarter Expert Advice🤫
We think this is how you could land Lead AWS Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field, especially those who work with AWS. A friendly chat can lead to insider info about job openings or even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your 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 scenarios. Be ready to discuss how you've tackled challenges in building scalable data pipelines and optimising performance. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Lead 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 any relevant technologies like Python or SQL. We want to see how your skills align with the role, so don’t be shy about showcasing your achievements!
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. Keep it engaging and relevant to the job description.
Showcase Your Problem-Solving Skills:In your application, mention specific examples where you've tackled complex technical challenges. We love seeing how you approach problems and what solutions you've implemented in past projects.
Apply Through Our Website:We encourage you to apply directly through our website for a smoother process. It helps us keep track of applications and ensures you get the best experience possible during your application journey!
How to prepare for a job interview at NTT America, Inc.
✨Know Your AWS Inside Out
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.
✨Showcase Your Data Engineering Skills
Prepare to talk about your experience with data pipelines, ETL/ELT workflows, and distributed data processing. Have specific examples ready that demonstrate your ability to design and optimise scalable data solutions.
✨Emphasise Collaboration and Leadership
Since this role involves working closely with architects and mentoring other engineers, be prepared to share examples of how you've successfully led teams or collaborated on complex projects. Highlight your communication skills and how you ensure everyone is aligned.
✨Be Ready for Technical Challenges
Expect some technical questions or case studies during the interview. Practice explaining your thought process when solving problems related to data quality, integrity, and performance. This will show your analytical skills and hands-on experience.
