Senior Data Engineer I

Joining Amex Tech means discovering and shaping your contribution to something big. Here, you can work alongside talented tech teams and build a unique career with the Powerful Backing of American Express. With a range of opportunities to work with the latest technologies, and a commitment to back the broader engineering community through open source, our mission is to power your success. Because Amex Tech is powered by our technology, our culture, and our colleagues.

Senior Data Engineer I is responsible for the architecture, design, engineering, and optimization of enterprise-scale data platforms that power mission-critical business capabilities. This role transforms logical data architectures into scalable, resilient, and secure physical implementations across relational, NoSQL, distributed, and cloud-native database technologies.

The Senior Data Engineer drives technical excellence by leading database architecture, administration, performance optimization, high availability, disaster recovery, and operational resiliency initiatives. Leveraging deep expertise in large-scale database systems, the role ensures optimal performance, scalability, security, and reliability while delivering highly available, cloud-native data solutions.

Working closely with Product, Architecture, Platform Engineering, and Business stakeholders, the Senior Data Engineer leads the adoption of modern data engineering practices, automation, Infrastructure as Code (IaC), and emerging database technologies. The role is instrumental in advancing enterprise data platforms through sophisticated data modeling, query optimization, partitioning, indexing, and distributed data management strategies, enabling high-performance, data-driven applications at scale.

Education:

• Bachelor's degree in Computer Science, Computer Engineering, Information Systems, or a related technical discipline; Master's degree preferred or equivalent professional experience.

 

Required Experience:

• 8+ years of experience designing, developing, administering, and optimizing large-scale (TB/PB) enterprise database platforms and data engineering solutions.
• Expert-level experience with relational databases including Oracle, PostgreSQL, and MySQL.
• Strong experience with NoSQL databases including MongoDB, Couchbase, Cassandra, Redis, or equivalent distributed NoSQL platforms.
• Experience with distributed databases including YugabyteDB, Cassandra or equivalent distributed SQL/NoSQL technologies. SingleStore experience is highly preferred.
• Experience with in-memory databases such as SingleStore, Redis, or  Apache Ignite.
• Extensive experience with cloud-native database platforms and Database-as-a-Service (DBaaS/SaaS) offerings on AWS and Google Cloud Platform (GCP), including Amazon RDS, Aurora, DynamoDB, Cloud SQL, BigQuery, Bigtable, MongoDB Atlas, Couchbase and Yugabyte.
• Demonstrated expertise in database performance tuning, including SQL optimization, execution plan analysis, indexing, partitioning, optimizer statistics, concurrency, locking, memory management, replication, storage optimization, and capacity planning, with measurable production results.
• Strong experience designing logical and physical data models using enterprise modeling tools such as ER/Studio, ERwin, or equivalent.
• Experience designing and supporting OLTP, OLAP, data warehouse, data mart, and Big Data platforms.
• Experience building scalable ETL/ELT, data integration, and distributed data processing solutions using technologies such as Apache Spark and Kafka.
• Strong programming skills in Python and SQL; experience with Java or other object-oriented languages is a plus.
• Experience with Infrastructure as Code (Terraform), Docker, Kubernetes, Git, Linux, shell scripting, and modern CI/CD practices.
• Experience with ServiceNow, Jira, or similar ITSM, ticketing, change management, incident management, and Agile project management platforms.
• Experience working within Agile software delivery methodologies, including Scrum, Kanban, and Test-Driven Development (TDD).

 

Technical Knowledge:

• Deep understanding of relational, NoSQL, distributed, and cloud-native database architectures, including storage engines, indexing strategies, query optimization, replication, encryption, backup/recovery, high availability (HA), disaster recovery (DR), and database security.
• Strong knowledge of distributed systems, multi-tier architectures, consensus algorithms, and scalable data platform design.
• Knowledge of Big Data ecosystems, data lake architectures, and modern data storage technologies.
• Understanding of XML, JSON, schema design, metadata management, and open-source database technologies.
• Knowledge of infrastructure and storage architectures, including SAN, NAS, hyper-converged infrastructure (e.g., Nutanix), and cloud-native storage solutions.
• Working knowledge of Artificial Intelligence (AI) and Generative AI (GenAI) technologies, including LLM integration, vector databases, retrieval-augmented generation (RAG), AI-assisted development, and AI-powered data engineering workflows.
• Strong understanding of observability, monitoring, SRE principles, and production operations.

 

Professional Attributes:

• Self-motivated, highly technical, and results-oriented with a strong sense of ownership.
• Excellent analytical, troubleshooting, and problem-solving skills.
• Proven ability to diagnose and resolve complex production issues across database, cloud, and distributed systems.
• Strong communication, collaboration, and technical leadership skills with experience mentoring engineers and influencing architectural decisions.
• Demonstrated success delivering highly scalable, resilient, secure, and high-performance enterprise data platforms.

 

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

 

• Mentor and coach Data Engineers while fostering a culture of technical excellence, innovation, knowledge sharing, and continuous improvement across engineering teams.
• Lead and actively contribute within Agile teams, partnering with Product, Architecture, and Business stakeholders to deliver scalable, high-quality data solutions and prioritize work across sprint cycles.
• Evaluate emerging database technologies and platform capabilities, driving the adoption of modern database features, cloud-native services, and engineering best practices across the organization.
• Design and implement scalable logical and physical data models that support high-performance, resilient, and secure enterprise data platforms.
• Engineer, administer, and optimize relational, NoSQL, distributed, and cloud-native database platforms, ensuring scalability, reliability, and operational excellence.
• Lead database performance optimization initiatives, including SQL tuning, execution plan analysis, indexing, partitioning, storage optimization, capacity planning, and workload management.
• Design and maintain highly available database architectures, replication strategies, backup and recovery processes, and disaster recovery solutions to ensure business continuity.
• Establish and enforce enterprise standards for data architecture, database security, governance, automation, and operational best practices.
• Develop Infrastructure as Code (IaC) solutions and automation frameworks to provision, configure, deploy, and manage database platforms efficiently.
• Design and optimize Big Data platforms by implementing advanced data modeling, partitioning, indexing, and distributed data management strategies.
• Partner with cross-functional engineering teams to integrate data platforms with cloud-native applications, CI/CD pipelines, containerized environments, and modern data engineering ecosystems.
• Collaborate closely with Product, Architecture, Security, and Business teams to align data platform capabilities with strategic business objectives and technology roadmaps.
• Lead root cause analysis for complex production incidents and drive continuous improvements in database reliability, observability, performance, and operational resilience.
• Influence technical direction by evaluating new technologies, establishing engineering standards, and driving modernization initiatives across enterprise data platforms.