Senior Data Engineer - AI & Intelligent Payment PlatformsAre you ready to help build the next generation of intelligent payment platforms?American Express is making strategic investments in next-generation payment network products, data platforms, and AI-powered capabilities to support its global growth agenda.
The American Express Card Network operates as a highly distributed, near-real-time, low-latency platform designed for exceptional availability and resiliency-processing transactions around the world, 24 hours a day, 365 days a year.
As a
Senior Data Engineer - AI & Intelligent Payment Platforms, you will help design, build, and evolve highly scalable, AI-ready data platforms that power critical payment capabilities and enable real-time intelligence, advanced analytics, machine learning, and Generative AI use cases.
You will work at the intersection of
data engineering, distributed systems, cloud-native technologies, and AI, helping transform large-scale payment data into intelligent, secure, and reliable products and experiences.
Responsibilities- Design and build highly scalable, resilient, and low-latency data platforms supporting mission-critical payment workloads.
- Develop distributed data processing and real-time streaming pipelines capable of handling high-volume transactional data.
- Build AI-ready data architectures and pipelines that enable machine learning, Generative AI, and real-time intelligence use cases.
- Explore and implement emerging AI capabilities, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, vector search, and AI agents, where appropriate.
- Design and develop microservices, APIs, and event-driven architectures using modern engineering practices.
- Drive adoption of cloud-native technologies, containerization, orchestration, CI/CD, automation, and observability.
- Establish engineering practices for performance, scalability, resiliency, security, data quality, and operational excellence.
- Apply Responsible AI and data governance principles, including privacy, security, explainability, model evaluation, and appropriate controls.
- Evaluate emerging technologies across AI, data, and distributed computing and translate them into practical solutions for payment platforms.
- Provide technical leadership, mentor engineers, conduct design reviews, and contribute to engineering standards and best practices.
- Partner with product, architecture, data science, and engineering teams to turn complex business opportunities into scalable technology solutions.
QualificationsMinimum Qualifications- Experience designing, implementing, and operating large-scale distributed data platforms and systems.
- Strong experience with NoSQL technologies such as Cassandra, Elasticsearch, Couchbase, or Redis.
- Experience with large-scale distributed data processing technologies such as Apache Spark.
- Strong programming experience with Python, Java, Scala, or similar languages.
- Experience designing data pipelines, data models, APIs, and event-driven architectures.
- Strong understanding of distributed system concepts, including scalability, reliability, resiliency, security, and performance.
- Demonstrated ability to solve complex engineering problems and deliver production-grade solutions.
Preferred Qualifications- Experience building AI/ML-ready data platforms supporting machine learning, Generative AI, or intelligent application use cases.
- Familiarity with Generative AI, LLMs, RAG, embeddings, vector databases/search, and AI agent architectures.
- Experience integrating AI/ML models and services into production environments.
- Familiarity with MLOps/LLMOps, including deployment, evaluation, monitoring, observability, governance, and lifecycle management.
- Experience with distributed messaging and streaming platforms such as Apache Kafka.
- Experience with real-time distributed processing using technologies such as Spark, Kafka, Cassandra, and Elasticsearch.
- Experience developing microservices and cloud-native applications.
- Experience with Docker, Kubernetes, OpenShift, or similar container platforms.
- Experience with CI/CD, DevOps, infrastructure automation, and production observability.
- Understanding of Responsible AI, data privacy, security, governance, and model risk considerations.
- Experience architecting large-scale systems with an emphasis on availability, scalability, performance, resiliency, and cost efficiency.
- Ability to stay current with emerging AI and data engineering technologies and identify opportunities to apply them to real-world business problems.
- Strong communication, collaboration, and technical leadership skills, with the ability to mentor engineers and influence technical direction.
Depending on factors such as business unit requirements, the nature of the position, cost and applicable laws, American Express may provide visa sponsorship for certain positions.
About the TeamWe back you with benefits that support your holistic well-being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally:
- Competitive base salaries
- Bonus incentives
- 6% Company Match on retirement savings plan
- Free financial coaching and financial well-being support
- Comprehensive medical, dental, vision, life insurance, and disability benefits
- Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need
- 20+ weeks paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption or surrogacy
- Free access to global on-site wellness centers staffed with nurses and doctors (depending on location)
- Free and confidential counseling support through our Healthy Minds program
- Career development and training opportunities
For a full list of Team Amex benefits, visit our Colleague Benefits Site.
The below represents the expected salary range for this job requisition. Ultimately, in determining your pay, we'll consider your location, experience, and other job-related factors.
