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Tier-1 brand, metro role, generalist mid-level data title, and broad GCP skillset increase applicant competition.
GCP- and BigQuery-specific platform expertise moderately limits cross-industry transferability.
Multiple mandatory GCP data platform, BigQuery, Spark, Airflow, and platform-service skills create moderately strict filters.
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Lead design and development of reusable data engineering platforms and capabilities on Google Cloud Platform (GCP) to improve scalability, performance, and reliability within the American Express Big Data ecosystem.
Design and build data pipelines that support Generation AI applications, including aspects like ingestion, chunking, embeddings, vector search, and hybrid retrieval processes.
Analyze unfamiliar systems to identify bottlenecks and architectural weaknesses and convert solutions into standardized platform capabilities adopted across multiple teams.
Strong expertise in GCP-native data engineering including BigQuery, Apache Spark, Python, and Airflow.
Experience building APIs and platform services using frameworks like Spring Boot and FastAPI on GCP.
Conceptual understanding of GenAI, Retrieval-Augmented Generation (RAG), LLM applications, and multi-agent AI architectures; hands-on GenAI experience is a plus.
Work Experience Required: Not explicitly mentioned in the JD.
Able to quickly learn and analyze unfamiliar architectures and technology stacks to resolve complex engineering challenges.
Experienced in mentoring engineers and promoting adoption of engineering standards and best practices.
Capable of evaluating distributed system trade-offs across latency, throughput, scalability, reliability, operational complexity, and cost in large-scale data platforms.