Data Platform Engineering Manager

Trammell Venture Partners, L.L.C.; United States; Remote; USD 180,000 - 250,000; Full time

Job summary

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

Qualifications

• 8+ years in data engineering, platform engineering, or distributed systems.
• Experience 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.

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 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

Data engineering

Platform engineering

Distributed systems

Team management

AWS data lakes

Kafka

Spark

Python/Scala/Java

Cloud-native data infra

Communication

AI/ML automation

Data governance

Real-time processing

Tools

RisingWave

ClickHouse

Spark

Kafka

Iceberg

Parquet

Presto

Job description

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 toindustry-leading security,crypto education, andworld-class client supportthrough our products likeKraken Pro,Desktop,Wallet, andKraken 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 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