Data Platform Manager - AI-Driven Infra
Listed on 2026-05-28
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IT/Tech
Data Engineering, AI Engineer (Applied/Software), Data Science Manager
Location: Town of Poland
Building the Future of Crypto
Our Krakenites are a world-class team with crypto conviction, united by our desire to discover and unlock the potential of crypto and blockchain technology.
Building the Future of Crypto
Our Krakenites are a world-class team with crypto conviction, united by our desire to discover and unlock the potential of crypto and blockchain technology.
What makes us different?
Kraken is a mission-focused company rooted in crypto values. As a Krakenite, you’ll join us on our mission to accelerate the global adoption of crypto, so that everyone can achieve financial freedom and inclusion. For over a decade, Kraken’s focus on our mission and crypto ethos has attracted many of the most talented crypto experts in the world.
Before you apply, please read the Kraken Culture page to learn more about our internal culture, values, and mission. We also expect candidates to familiarize themselves with the Kraken app. Learn how to create a Kraken account here.
As a fully remote company, we have Krakenites in 70+ countries who speak over 50 languages. Krakenites are industry pioneers who develop premium crypto products for experienced traders, institutions, and newcomers to the space. Kraken is committed to industry-leading security, crypto education, and world-class client support through our products like Kraken Pro, Desktop, Wallet, and Kraken Futures.
Become a Krakenite and build the future of crypto!
Proof of work
The team
Kraken's Data Platform team builds the real-time infrastructure that powers decision-making across one of the world's largest digital asset exchanges. We operate at the intersection of streaming data, large-scale platform engineering, and AI-driven automation — processing billions of events daily across trading, compliance, and product systems.
This role leads the team responsible for Kraken's streaming and data platform layer — designing systems that move, transform, and serve data in real time. You'll own the architecture around stream processing (Rising Wave, Flink), drive adoption of AI-powered automation and workflows across the data stack, and build the platform primitives that the rest of engineering depends on.
The opportunity
- Lead and grow a team of senior data platform engineers building Kraken's real-time streaming infrastructure
- Own the architecture and roadmap for high-volume low-frequency data systems, with focus on data stack like Spark, Kafka, Iceberg, Rising Wave, Apache Flink
- Design and operate scalable data architecture that serve trading, risk, compliance, analytics and many product teams.
- Drive adoption of AI automation and intelligent workflows — automating data quality checks, pipeline orchestration, anomaly detection, and self-healing infrastructure
- Partner with ML/AI, analytics, and product engineering teams to deliver platform capabilities that accelerate their work
- Evolve Kraken's data-lake and warehouse architecture to support both batch and streaming workloads seamlessly
- Set technical direction for the team — balancing reliability, velocity, and cost efficiency at scale
- Hire, mentor, and retain top-tier platform engineers; build a culture of ownership and technical excellence
- 8+ years in data engineering, platform engineering, or distributed systems — with at least 3 years managing engineering teams
- Experience and knowledge of building data-lakes in AWS (i.e. Spark, Athena, Iceberg, Parquet, Presto), including data modeling, data quality best practices, and self-service tooling.
- Strong expertise in building and operating real-time data at scale including Kafka, Spark Streaming, Debezium, and CDC pipelines.
- Proven ability to manage competing priorities across multiple stakeholder groups — aligning platform investments with the needs of product, finance, compliance, analytics, and other teams
- Strong communicator — able to explain risks, trade-offs, and roadmap decisions to both senior technical audiences and non-specialist stakeholders.
- Experience designing or adopting AI/ML-powered automation in data workflows — pipeline orchestration, intelligent monitoring, automated remediation, or LLM-integrated tooling
- Proficiency in Python, Scala,…
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