Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer II, ROW AOP

Amazon
London
4 days ago
Create job alert

Job ID: 3038794 | Amazon (China) Holding Company Limited

The AOP (Analytics Operations and Programs) team is responsible for creating core analytics, insight generation and science capabilities for ROW Ops. We develop scalable analytics applications, AI/ML products and research models to optimize operation processes. You will work with Product Managers, Data Engineers, Data Scientists, Research Scientists, Applied Scientists and Business Intelligence Engineers using rigorous quantitative approaches to ensure high quality data/science products for our customers around the world. As a Data Engineer, you will play a crucial role in supporting the team by creating and maintaining the data infrastructure necessary for the advanced analytics and machine learning solutions.

Our team solves a broad range of problems that can be scaled across ROW (Rest of the World including countries like India, Australia, Singapore, MENA and LATAM). Here is a glimpse of the problems that this team deals with on a regular basis:

• Using live package and truck signals to adjust truck capacities in real-time
• HOTW models for Last Mile Channel Allocation
• Using LLMs to automate analytical processes and insight generation
• Ops research to optimize middle mile truck routes
• Working with global partner science teams to affect Reinforcement Learning based pricing models and estimating Shipments Per Route for $MM savings
• Deep Learning models to synthesize attributes of addresses
• Abuse detection models to reduce network losses

Key job responsibilities
1. Design, develop, and maintain scalable data pipelines to support ML model development and production deployment.
2. Implement and maintain CI/CD pipelines for the data and ML solutions.
3. Collaborate with data scientists and other team members to understand data requirements and implement efficient data processing solutions.
4. Create and manage data warehouses and data lakes, ensuring proper data governance and security measures are in place.
5. Collaborate with product managers and business stakeholders to understand data needs and translate them into technical requirements.
6. Stay current with emerging technologies and best practices in data engineering, and propose innovative solutions to improve data infrastructure and processes for ML models and analytics applications.
7. Participate in code reviews and contribute to the development of best practices for data engineering within the team.

About the team
The AOP (Analytics Operations and Programs) team is responsible for creating core analytics, insight generation and science capabilities for ROW Ops. We develop scalable analytics applications, AI/ML products and research models to optimize operation processes. You will work with Product Managers, Data Scientists, Research Scientists, Applied Scientists and Business Intelligence Engineers using rigorous quantitative approaches to ensure high quality data/science products for our customers around the world.

BASIC QUALIFICATIONS

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 3+ years of experience in data engineering or related roles.
- Strong programming skills in languages such as Python, Java, or Scala.
- Expertise in SQL and experience with both relational and NoSQL databases.
- Familiarity with cloud platforms (e.g., AWS) and their services.
- Knowledge of data modeling, data warehousing, and ETL design patterns.
- Experience with version control systems (e.g., Git) and CI/CD pipelines .
- Strong problem-solving skills and attention to detail.
- Excellent communication skills and ability to work in a collaborative team environment.

PREFERRED QUALIFICATIONS

- Experience working in a scientific or research-oriented environment.
- Familiarity with machine learning workflows and model deployment.
- Experience with Infrastructure as Code (IaC) by tools such as CDK.
- Experience with streaming data processing and real-time analytics.
- Experience with big data technologies (e.g., Hadoop, Spark, Hive).

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability or other legally protected status.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Engineer II

Data Engineer II

Data Engineer II

Data Engineer II

Data Engineer II (Manchester/Hybrid, UK)

Data Engineer II, IN Ads

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Top 10 Skills in Machine Learning According to LinkedIn & Indeed Job Postings

Machine learning (ML) is at the forefront of innovation, powering systems in finance, healthcare, retail, logistics, and beyond in the UK. As organisations leverage ML for predictive analytics, automation, and intelligent systems, demand for skilled practitioners continues to grow. So, which skills are most in demand? Drawing on insights from LinkedIn and Indeed, this article outlines the Top 10 machine learning skills UK employers are looking for in 2025. You'll learn how to demonstrate these capabilities through your CV, interviews, and real-world projects.

The Future of Machine Learning Jobs: Careers That Don’t Exist Yet

Machine learning (ML) has become one of the most powerful forces reshaping the modern world. From voice assistants and recommendation engines to fraud detection and medical imaging, it underpins countless applications. ML is no longer confined to research labs—it powers business models, public services, and consumer technologies across the globe. In the UK, demand for machine learning professionals has risen dramatically. Organisations in finance, retail, healthcare, and defence are embedding ML into their operations. Start-ups in Cambridge, London, and Edinburgh are pioneering innovations, while government-backed initiatives aim to position the UK as a global AI leader. Salaries for ML engineers and researchers are among the highest in the tech sector. Yet despite its current importance, machine learning is only at the beginning of its journey. Advances in generative AI, quantum computing, robotics, and ethical governance will reshape the profession. Many of the most vital machine learning jobs of the next two decades don’t exist today. This article explores why new careers will emerge, the roles likely to appear, how today’s roles will evolve, why the UK is well positioned, and how professionals can prepare now.

Seasonal Hiring Peaks for Machine Learning Jobs: The Best Months to Apply & Why

The UK's machine learning sector has evolved into one of Europe's most intellectually stimulating and financially rewarding technology markets, with roles spanning from junior ML engineers to principal machine learning scientists and heads of artificial intelligence research. With machine learning positions commanding salaries from £32,000 for graduate ML engineers to £160,000+ for senior principal scientists, understanding when organisations actively recruit can dramatically accelerate your career progression in this pioneering and rapidly evolving field. Unlike traditional software engineering roles, machine learning hiring follows distinct patterns influenced by AI research cycles, model development timelines, and algorithmic innovation schedules. The sector's unique combination of mathematical rigour, computational complexity, and real-world application requirements creates predictable hiring windows that strategic professionals can leverage to advance their careers in developing tomorrow's intelligent systems. This comprehensive guide explores the optimal timing for machine learning job applications in the UK, examining how enterprise AI strategies, academic research cycles, and deep learning initiatives influence recruitment patterns, and why strategic timing can determine whether you join a groundbreaking AI research team or miss the opportunity to develop the next generation of machine learning algorithms.