Data Platform Engineering Manager
Leads a team of senior data platform engineers building real-time streaming infrastructure for Kraken's crypto exchange. Owns architecture for stream processing, data lakes, AI automation, and scalable data systems serving trading, compliance, and analytics teams. Requires 8+ years data/platform engineering and 3+ years management.
About the job
Responsibilities
- 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.
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.
Skills
Spark, Kafka, Iceberg, Risingwave, Apache Flink, AWS, Athena, Parquet, Presto, Debezium, Python, Scala, Java, Glue, S3
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