Data Engineer II

The Digital Workplace Data & Analytics Platform with AI ML capabilities aims to bring together the data from all Unified Workspace, Collaboration and Colleague Servicing platforms, combining this with HR, Information Security, and network data to provide real-time, meaningful insights in areas such as user experience, health scoring, productivity, and overall IT visibility. As the Engineer 2 of the Digital Workplace Data & Analytics Platform, you will have responsibility for leading the engineering teams to develop the Advance Data engineering pipeline, Data Management, Data DevOps on Cloud platform and enhance it to provide personalization capabilities, analytics, and engineering automations, and best practices. Our winning aspiration is to deliver the best Colleague digital experience. We simplify work and raise productivity by empowering Colleagues with the best digital tools and services. Opportunity for Impact Digital Workplace at American Express is entering into a new phase of technology transformation driven by opportunities to improve Colleague experience, raise productivity and collaboration, and drive operational efficiency of all service and infrastructure operations. If you have the talent and desire to deliver innovative products and services at a rapid pace, with hands on experience and strategic thinking, in areas of productivity and collaboration software suites, endpoint computing and security, mobile platforms, data management and analytics, and software engineering, join our leadership team to help with our transformation journey. The Data & Analytics platform with AI ML capabilities is central to the future of how we work and improve colleague experience while identifying opportunities for improvement. As the engineer of this group, you will: Design and develop reusable Python-based frameworks for batch and real-time data ingestion from APIs, files, event streams, webhooks, and messaging systems. Build scalable data pipelines on Google Cloud Platform using cloud-native services and modern distributed data processing technologies. Develop reliable, observable, production-grade data pipelines with strong monitoring, auditing, lineage, and operational excellence. Build reusable libraries and platform components that simplify data onboarding, transformation, orchestration, and pipeline lifecycle management. Develop data quality, schema validation, and metadata-driven ingestion capabilities to improve platform reliability. Build cloud-native microservices and APIs using Python for exposing data services and platform capabilities. Develop containerized applications using Docker and deploy services on Kubernetes/GKE. Contribute to platform observability using logging, monitoring, metrics, dashboards, and alerting. Collaborate with architects, product owners, data engineers, ML engineers, and business teams to build scalable enterprise data platforms. Participate in architecture discussions, code reviews, technical design, and engineering best practices. Continuously improve engineering standards through automation, reusable frameworks, CI/CD, testing, and infrastructure as code. A portfolio showcasing previous Cloud based Data & Analytics projects, contributions to open-source projects, or relevant publications is a plus. Build culture of innovation, ownership, accountability, and customer focus Contribute to the American Express Data & Analytics Strategy. Working with other Technology teams to drive enterprise solutions, define best practice at a company level and further develop skills and experience outside Digital Workplace. Strengthen the collaboration with Industry partners/suppliers for more robust data solutions and market research for innovative solutions in this space. Required Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or a related technical discipline with 3+ years of experience in software or data engineering. Strong proficiency in Python and SQL, with experience designing and developing scalable, reusable, and production-grade applications. Hands-on experience building batch and real-time data pipelines using APIs, files, event streams, or messaging systems. Experience with cloud platforms (GCP preferred) and cloud-native technologies including Docker, Kubernetes, CI/CD, and Git. Good understanding of distributed data processing, data modeling, data quality, and data engineering best practices. Experience developing RESTful APIs, microservices, or reusable platform components with a focus on scalability, reliability, and observability. Strong problem-solving, communication, and collaboration skills with the ability to work effectively in an Agile environment. Preferred Qualifications: Experience building reusable data ingestion, orchestration, or data quality frameworks for enterprise-scale data platforms. Hands-on experience with GCP services such as BigQuery, Pub/Sub, GCS, GKE, Cloud Run, or equivalent cloud technologies. Experience with Apache Spark, Kafka, Airflow (or similar orchestration tools), Elasticsearch/Kibana, and modern data engineering ecosystems. Exposure to data visualization tools like Tableau, PowerBI, Grafana etc. Knowledge of metadata management, data catalog, data lineage, semantic data models, or data governance concepts. Experience implementing authentication and authorization (OAuth2, JWT, IAM), monitoring, logging, and observability for production systems. Exposure to infrastructure as code, DevOps practices, and AI/ML data platform technologies is a plus. Ideal Candidate Profile: The ideal candidate is a software engineer who enjoys building engineering platforms rather than one-off pipelines. They are comfortable designing reusable Python frameworks, building cloud-native services, and developing reliable distributed data systems. They have a strong ownership mindset, enjoy solving platform-scale problems, and are passionate about improving developer productivity through reusable tooling and automation.