Requirements

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, or Java in a production data platform context
Solid understanding of cloud-native data infrastructure (AWS preferred — Glue, Athena, S3, EMR, Lambda, or equivalents)
Track record of managing, recruiting, and developing high-performing remote engineering teams
Ability to translate long-term platform vision into executable quarterly roadmaps
Servant-leadership style — you coach, unblock, and grow your engineers
AI-ready to 10X the team efficiency and overall output

Nice to haves

Experience with RisingWave and/or Clickhouse specifically — either in production or in serious evaluation
Familiarity with LLM-based agents or AI workflow frameworks (e.g. LangChain, LangGraph, custom orchestration)
Background in cryptocurrency, trading systems, or high-throughput financial data
Experience building self-service data platform tooling for internal engineering consumers
Contributions to open-source streaming or data infrastructure projects

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