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About the role
5+ years of professional experience designing and developing cloud-native data engineering solutions and scalable data pipelines. Strong experience building batch and streaming data pipelines using technologies such as Python, PySpark, Spark, Dataproc, SQL, or equivalent distributed data processing frameworks. Experience developing on Google Cloud Platform (preferred) or AWS/Azure using services such as BigQuery, Cloud Storage, Pub/Sub, Dataproc, Cloud Functions, Cloud Run, or equivalent cloud-native data services. Experience designing and implementing ETL/ELT pipelines, data transformation frameworks, canonical data models, and data quality controls for enterprise-scale data platforms. Experience integrating enterprise applications and cloud platforms using REST APIs, event streaming, webhooks, messaging technologies, and batch or real time data ingestion. Strong experience with Apache Kafka, Google Pub/Sub, or similar messaging and event streaming platforms. Strong proficiency in SQL, data modeling, performance optimization, and working with relational and NoSQL databases supporting analytical and operational workloads. Familiarity with CCaaS platforms and customer interaction data, including voice, chat, IVR, dialers, Agent Assist, STT/TTS, conversation transcripts, interaction events, and operational metrics. Experience supporting enterprise analytics through BigQuery, semantic datasets, data virtualization, reusable data products, or Metrics as a Service platforms. Experience working in Agile environments with Git, CI/CD, DevOps practices, and cloud deployment automation. Strong analytical, troubleshooting, and problem-solving skills with the ability to independently own technical deliverables and mentor junior engineers. Excellent communication and collaboration skills with the ability to work effectively across Product, Engineering, Architecture, Analytics, and Data teams. Good to Have: Experience with Java and Spring Boot for backend services supporting data platforms or APIs. Exposure to Context Management, Customer Journey Analytics, Contact Center Analytics, Customer Interaction Platforms, or enterprise data virtualization. Familiarity with Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, semantic search, or AI-powered analytics. Experience with workflow orchestration platforms such as Apache Airflow, Cloud Composer, or similar scheduling frameworks. Experience modernizing enterprise data platforms or migrating workloads to Google BigQuery and cloud-native analytics environments. Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.
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