Sr Data Engineer II
About The Position
The Senior Data Engineer – AI & Intelligent Payment Platforms will help design, build, and evolve scalable, AI-ready data platforms that support critical payment capabilities and enable real-time intelligence, advanced analytics, machine learning, and Generative AI use cases. The role operates at the intersection of data engineering, distributed systems, cloud-native technologies, and AI, transforming large-scale payment data into intelligent, secure, and reliable products and experiences. The American Express Card Network is a highly distributed, near-real-time, low-latency platform designed for availability and resiliency, processing transactions worldwide 24 hours a day, 365 days a year. The engineer will partner with product, architecture, data science, and engineering teams, while providing technical leadership and mentoring. The position focuses on production-grade payment platforms and practices that support scalability, resiliency, security, data quality, governance, and operational excellence. American Express may provide visa sponsorship for certain positions depending on business requirements, role, cost, and applicable laws. The position offers comprehensive well-being support and opportunities to learn, develop leadership skills, and grow professionally. The working arrangement may be onsite, hybrid, or virtual depending on the role and business need. The salary range is determined with consideration of location, experience, and other job-related factors; no range values are provided in the posting.
Requirements
- 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.
Nice To Haves
- Experience building AI/ML-ready data platforms for machine learning, Generative AI, or intelligent applications.
- Familiarity with Generative AI, LLMs, RAG, embeddings, vector databases or 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 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, including the ability to mentor engineers and influence technical direction.
Responsibilities
- Design and build scalable, resilient, low-latency data platforms for mission-critical payment workloads.
- Develop distributed data processing and real-time streaming pipelines for high-volume transactional data.
- Build AI-ready data architectures and pipelines supporting machine learning, Generative AI, and real-time intelligence use cases.
- Explore and implement AI capabilities such as LLMs, RAG, embeddings, vector search, and AI agents where appropriate.
- Design and develop microservices, APIs, and event-driven architectures using modern engineering practices.
- Promote 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 AI, data, and distributed computing technologies and translate them into practical payment-platform solutions.
- 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 business opportunities into scalable technology solutions.
Benefits
- 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, offered for pregnancy, adoption, or surrogacy
- Free access to global onsite wellness centers staffed with nurses and doctors, depending on location
- Free and confidential counseling support through the Healthy Minds program
- Career development and training opportunities
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
