Senior Data Engineer, AI Platform

• Shape the data platform powering analytics, machine learning, Generative AI, and Agentic AI for millions of customers and internal teams.
• Build scalable, cloud-native data infrastructure across batch, streaming, structured, and unstructured workloads using modern engineering best practices.
• Partner with Data Science, Machine Learning, Product, and Engineering leaders to deliver AI-ready data products that accelerate innovation across the business.
• Influence the future of Zip's Data & AI Platform by driving architecture, mentoring engineers, and establishing the foundations for enterprise-scale AI.
• Remote-first opportunity for US-based employees with the option to work in-person out of our Manhattan office.

About us

We are Zip, a global Buy Now, Pay Later company providing fair and seamless solutions that simplify how millions of people pay. Our journey began in Australia, and we’ve grown significantly since coming to America.

We exist to create a world where people can live fearlessly today, knowing they’re in control of tomorrow. Focused on product innovation that puts people at the center, we put the financial well-being of our customers and merchant partners at the heart of everything that we do.

We’re proud to be a values-led business. They guide us in everything we do - how we work together and create game-changing experiences for our customers and fellow Zipsters.

About the Role

Zip is building the next generation of AI-powered experiences for customers, employees, and partners. As a Senior Data Engineer on the Data & AI Platform team, you will design, build, and scale the data foundation that powers analytics, machine learning, Generative AI, and Agentic AI across the organization.

You will develop secure, scalable, and reliable data platforms that support structured and unstructured data processing, real-time and batch workloads, AI-ready data products, and intelligent retrieval capabilities. Working closely with Data Scientists, ML Engineers, Software Engineers, Product teams, and business stakeholders, you will help create a modern data ecosystem that accelerates innovation while maintaining the highest standards of quality, governance, and operational excellence.

This is a high-impact role with the opportunity to shape the future of Zip’s AI and data platform strategy.

What You'll Do

• Design, build, and scale Zip’s enterprise Data & AI Platform supporting analytics, machine learning, Generative AI, and Agentic AI initiatives.
• Develop scalable data pipelines and architectures for structured, unstructured, batch, and real-time data workloads.
• Build AI-ready data products, semantic models, and retrieval capabilities that enable intelligent agents and AI-powered applications.
• Design and optimize vector search, hybrid search, and Retrieval-Augmented Generation (RAG) capabilities to improve AI effectiveness and user experiences.
• Implement streaming and event-driven solutions that support near real-time business insights, operational intelligence, fraud detection, and risk monitoring.
• Establish engineering best practices for data modeling, testing, observability, governance, security, and platform reliability.
• Drive self-service and automation capabilities that improve developer productivity and accelerate time-to-insight.
• Partner with cross-functional teams to deliver trusted, high-quality data solutions that support critical business and AI initiatives.
• Mentor engineers, influence architecture decisions, and help define the future direction of Zip’s data platform.

What You'll Bring

• 8+ years of experience designing, building, and operating enterprise-scale data platforms.
• Deep expertise with modern cloud data platforms such as Snowflake, Databricks, Microsoft Fabric, BigQuery, Redshift, or similar technologies.
• Advanced SQL and Python skills with strong experience in data modeling, ELT/ETL development, dbt, and orchestration frameworks such as Airflow, Dagster, or equivalent.
• Experience building scalable streaming and event-driven architectures using technologies such as Kafka, Azure Event Hubs, Spark, or similar platforms.
• Strong understanding of modern data architecture patterns, including dimensional modeling, semantic layers, data governance, metadata management, and platform observability.
• Experience supporting AI and machine learning