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- Tasks: Lead the design and implementation of data pipelines and ML infrastructure on AWS.
- Company: Join a cutting-edge team focused on high-impact machine learning solutions.
- Benefits: Enjoy remote work flexibility and potential contract renewal opportunities.
- Why this job: Be part of innovative projects and influence best practices from day one.
- Qualifications: 5+ years in data engineering, with strong AWS, Python, and SQL skills required.
- Other info: Experience in high-compliance industries is a plus.
The predicted salary is between 60000 - 84000 £ per year.
Location: London | Duration: 6 Months (Extension Possible) | Start Date: ASAP
Are you a cloud-native data engineer with a passion for building intelligent systems? I’m looking for a Senior AWS Machine Learning Data Engineer to join a cutting-edge team on a contract basis, working on high-impact ML solutions that scale.
About the Role:
In this role, you’ll lead the design and implementation of data pipelines and ML infrastructure on AWS. You’ll collaborate with data scientists, architects, and product teams to deliver production-grade ML systems across a variety of domains — from predictive analytics to real-time personalization.
What You’ll Do:
- Architect and build scalable, secure, and maintainable data pipelines on AWS (Glue, Lambda, Step Functions, S3, Redshift, etc.)
- Operationalize machine learning models in production environments using SageMaker or custom deployment frameworks
- Automate and optimize ETL/ELT processes for structured and unstructured data
- Integrate CI/CD and MLOps best practices for rapid model iteration and deployment
- Collaborate across teams to align ML engineering efforts with business needs
What We’re Looking For:
- 5+ years of data engineering experience, with 2+ years in a senior role
- Deep expertise with AWS services (S3, Glue, Lambda, SageMaker, Redshift, etc.)
- Strong Python and SQL skills; experience with PySpark a bonus
- Familiarity with containerization (Docker), orchestration (Airflow, Step Functions), and infrastructure as code (Terraform/CDK)
- Solid understanding of machine learning model lifecycle and best practices for deployment at scale
- Excellent communication skills and the ability to work independently in a remote environment
- Experience with real-time data streaming (Kafka, Kinesis)
- Exposure to data governance and security best practices in cloud environments
- Previous work in a high-compliance industry (finance, healthcare, etc.)
Why Join?
- Work with a passionate, experienced team on leading-edge projects
- Influence architecture and best practices from day one
- Opportunity for contract renewal
Senior Data Engineer - AWS - Machine Learning employer: InterQuest Solutions
Contact Detail:
InterQuest Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer - AWS - Machine Learning
✨Tip Number 1
Make sure to showcase your experience with AWS services in your conversations. Highlight specific projects where you've used tools like Glue, Lambda, and SageMaker to demonstrate your hands-on expertise.
✨Tip Number 2
Prepare to discuss your approach to building scalable data pipelines. Be ready to explain the architecture you’ve implemented in previous roles and how it aligns with best practices in machine learning deployment.
✨Tip Number 3
Familiarise yourself with the latest trends in MLOps and CI/CD practices. Being able to articulate how you’ve integrated these into your workflow will set you apart from other candidates.
✨Tip Number 4
Network with professionals in the field, especially those who have worked in high-compliance industries. This can provide insights into industry-specific challenges and help you tailor your discussions during interviews.
We think you need these skills to ace Senior Data Engineer - AWS - Machine Learning
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your relevant experience in data engineering, particularly with AWS services and machine learning. Use specific examples that demonstrate your expertise in building data pipelines and operationalising ML models.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for cloud-native data engineering and your ability to collaborate with cross-functional teams. Mention specific projects or achievements that align with the job description to make your application stand out.
Highlight Technical Skills: Clearly list your technical skills related to AWS, Python, SQL, and any other relevant technologies like Docker or Terraform. Be sure to mention your experience with real-time data streaming and any familiarity with MLOps practices.
Showcase Communication Skills: Since excellent communication is key for this role, include examples of how you've effectively communicated complex technical concepts to non-technical stakeholders. This will demonstrate your ability to work independently and collaboratively in a remote environment.
How to prepare for a job interview at InterQuest Solutions
✨Showcase Your AWS Expertise
Make sure to highlight your experience with AWS services like S3, Glue, and SageMaker. Be prepared to discuss specific projects where you implemented these technologies, as this will demonstrate your hands-on knowledge and ability to lead in a cloud-native environment.
✨Demonstrate Your Machine Learning Knowledge
Since the role involves operationalising machine learning models, be ready to explain the ML model lifecycle. Discuss any relevant experience you have with deploying models in production, particularly using tools like SageMaker or custom frameworks.
✨Prepare for Technical Questions
Expect technical questions related to data pipelines, ETL/ELT processes, and CI/CD practices. Brush up on your Python and SQL skills, and be ready to solve problems on the spot, as this will showcase your technical prowess and problem-solving abilities.
✨Emphasise Collaboration Skills
This role requires collaboration with various teams, so be prepared to discuss how you've worked with data scientists, architects, and product teams in the past. Highlight your communication skills and any experiences that demonstrate your ability to align engineering efforts with business needs.
