At a Glance
- Tasks: Lead the design and optimisation of scalable data pipelines on AWS.
- Company: Join a forward-thinking tech company focused on Data & AI solutions.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on mentorship and career development.
- Why this job: Make a real impact by solving complex data challenges with cutting-edge technology.
- Qualifications: Expertise in AWS, Python/PySpark, and strong data engineering skills required.
The predicted salary is between 70000 - 90000 £ per year.
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.
- 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
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
- 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 the job involves:
We are seeking an accomplished and detail-oriented Lead Data Engineer – AWS to join our Data & AI practice. 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.
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.
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
- Configuration-driven frameworks (e.g., YAML)
Implement robust data ingestion, transformation, and processing patterns. Build reusable data services, components, and frameworks. Define and implement testing strategies for data pipelines, ensuring reliability and accuracy. Validate data processing workflows using:
- Python / PySpark transformations
- SQL-based validation logic
- Configuration-driven orchestration
Develop automated testing, monitoring, and alerting solutions. Ensure:
- Data completeness
- Data accuracy
- Consistent transformation behaviour
Drive improvements in observability and pipeline resilience. Lead development on AWS services including:
- S3-based data lakes
- Supporting services within the AWS data ecosystem
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.
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. Ensure data quality, integrity, and reliability across data platforms. Implement and enforce secure coding and data handling practices. Support compliance with:
- GDPR
- Regulated environment standards (where applicable)
Contribute to monitoring, auditing, and operational processes.
Lead Data Engineer (AWS Data) employer: NTT DATA
Join a forward-thinking company that values innovation and collaboration, where as a Lead Data Engineer, you will have the opportunity to work with cutting-edge AWS technologies in a dynamic environment. Our commitment to employee growth is reflected in our supportive culture, offering mentorship and professional development opportunities, while our focus on client-facing projects ensures that your contributions make a meaningful impact. Located in a vibrant area, we provide a stimulating workplace that encourages creativity and excellence in data engineering.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Engineer (AWS Data)
✨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 that could land you an interview.
✨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 your technical knowledge and problem-solving skills. Be ready to discuss your experience with ETL/ELT workflows and distributed processing frameworks. Practice common interview questions to boost your confidence!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Lead Data Engineer (AWS Data)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Lead Data Engineer role. Highlight your experience with AWS, data pipelines, and any relevant projects that showcase your skills in data engineering and cloud platforms.
Showcase Your Technical Skills:Don’t hold back on showcasing your technical skills! Mention your hands-on experience with AWS Glue, Python/PySpark, and SQL. We want to see how you’ve used these tools to solve real-world problems.
Communicate Clearly:Your written application should reflect your strong communication skills. Be clear and concise in your descriptions, and make sure to explain your thought process when discussing past projects or challenges.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and get you into our system quickly!
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. Be ready to discuss how you've used these tools in past projects, and think about specific examples where you optimised data pipelines or improved performance.
✨Show Off Your Data Engineering Skills
Prepare to talk about your hands-on experience with Python/PySpark and SQL. Have a few scenarios in mind where you tackled complex data processing challenges, and be ready to explain your thought process and the outcomes.
✨Demonstrate Leadership and Mentoring
Since this role involves leading teams, think about times when you've mentored others or led a project. Be prepared to share how you foster collaboration and ensure high-quality delivery while remaining technically involved.
✨Be Ready for Problem-Solving Questions
Expect some technical problem-solving questions during the interview. Practice explaining your approach to troubleshooting issues in data pipelines or cloud environments, and highlight your analytical skills and ability to think on your feet.
