Data Platform Engineering Manager

Kraken
United StatesPosted 24 April 2026

Job Description

Data Platform Engineering Manager 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 https://www.kraken.com/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 https://support.kraken.com/hc/en-us/articles/226090548-How-to-create-an-account-on-Kraken. 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 https://blog.kraken.com/crypto-education/security-at-kraken, crypto education https://blog.kraken.com/category/crypto-education, and world-class client support https://blog.kraken.com/crypto-education/support-at-kraken through our products like Kraken Pro https://pro.kraken.com/, Desktop https://www.kraken.com/desktop, Wallet https://www.kraken.com/wallet, and Kraken Futures https://www.kraken.com/features/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 (RisingWave, 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, RisingWave, 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 SKILLS YOU SHOULD HODL - 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 pri ... (truncated, view full listing at source)
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