Senior Data Engineer - Agents Systems

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Building the Future of Open Finance

Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system.

Before you apply, we encourage you to explore our culture page to understand what drives us and how we work.

Proof of work

The team

Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.

The Agent Systems team is a 0→1 engineering group building internal AI-powered agents that interact directly with company systems. The mandate is to dramatically increase internal speed of execution through automation, intelligent inference, and workflow orchestration.

This team operates at the intersection of AI, backend systems, and applied product engineering. The work is pragmatic and fast-moving — prototypes are expected, but the bar for production reliability remains high. Engineers on this team move fluidly between experimentation and shipping, building systems that reason over user interactions and take meaningful action across internal tools.

You’ll work closely with cross-functional partners across product, infrastructure, and internal operations to deploy agent-driven capabilities that compound leverage across the organization.

The opportunity

• Own and evolve streaming data pipelines that power live inference and real-time model serving across Kraken's AI infrastructure

• Design and build feature stores that serve low-latency, high-reliability features to production ML models

• Implement and maintain streaming systems using RisingWave, Apache Flink, or Kafka Streams, selecting the right tool for the workload

• Partner with ML engineers and AI infra teams to define data contracts, feature schemas, and pipeline SLAs

• Drive pipelines toward real-time where batch exists today reducing latency from hours to seconds

• Ensure data quality, observability, and auditability across all streaming and feature engineering systems

• Contribute to inference pipeline tooling where data engineering and model serving intersect

• Evaluate emerging streaming and feature store technologies and shape the team's technical roadmap

What You Bring

• 5+ years in data engineering with at least 2 years focused on streaming systems in production

• Hands-on experience with RisingWave, Apache Flink, Kafka Streams, or comparable stream processing frameworks

• Strong understanding of feature store design — online/offline consistency, point-in-time correctness, low-latency serving

• Experience building data pipelines that feed production ML models or inference systems