Software Development Engineer , AWS Payments

Amazon
London
5 days ago
Applications closed

Related Jobs

View all jobs

Software Engineer (Python React)

Lead Software Engineer

Senior Data Engineer (software)

Head of Software Development

Lead Data Engineer

Machine Learning Engineer, AWS Generative AI Innovation Center

Software Development Engineer, AWS Payments

Machine learning, big data; real-time data streaming. If these areas resonate with you, then join us to work on extremely motivating challenges at Amazon Web Services (AWS). Within AWS Payments we build and run Machine Learning models to optimize business processes and improve the customer experience.

If you are a strong software engineer, self-starter and learner who is passionate about working with massive amounts of data to build state-of-art systems on top of AWS native services, then this is the right opportunity for you. You will work with a team of highly skilled engineers and scientists to build the next generation Machine Learning, Data, and Analytics platform at AWS. As part of your job, you will deal with large amounts of training data, rapid prototyping of new models, performance optimizations, offline and online testing, and building fully automated solutions to push Machine Learning models to production, applying MLOps best practices.

As a software development engineer of this team, you will play a pivotal role in shaping the definition, vision, design, roadmap and development of this set of product features from beginning to end. You will:

  1. Mentor and lead junior developers on the team.
  2. Help drive business decisions with your technical input.
  3. Design, implement, test, deploy and maintain innovative software solutions, while optimizing service performance, durability, cost, and security.
  4. Use software engineering best practices to ensure a high standard of quality for all of the team deliverables.
  5. Participate in the full development cycle for ETL: design, implementation, validation, documentation, and maintenance.
  6. Design, implement, and support data warehouse / data lake infrastructure using AWS big data stack, Python, Redshift, Glue/lake formation, EMR/Spark/Scala, Athena etc.
  7. Write high quality distributed and scalable systems, to deal with large scale data.
  8. Automate the end-to-end development life-cycle to deploy Machine Learning models from research phase to production.
  9. Work in an agile, startup-like development environment, where you are always working on the most important stuff.
  10. Work closely with scientists, data engineers and other stakeholders to create and deploy new features, in order to optimize various business processes.


In this role you will contribute to a critical and highly-visible function within the AWS business. You will be given the opportunity to autonomously deliver the technical direction of new projects and features in our roadmap. If you’re excited to have a large impact on AWS and the cloud computing industry, you’ll find this role to be engaging, challenging, and full of opportunities to learn and grow.

BASIC QUALIFICATIONS

- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one software programming language

PREFERRED QUALIFICATIONS

- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- Experience building and optimizing ‘big data’ pipelines, architectures and data sets
- Experience using big data technologies (Hive, Hbase, Spark, EMR, etc.)
- Advanced working SQL knowledge and experience working with relational databases

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status.

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.

The base salary for this position ranges from $114,800/year up to $191,800/year. Salary is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. Applicants should apply via our internal or external career site.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.