Data Platform Engineering Manager

Kraken; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

Kraken is looking for a Data Platform Team Lead in Greater London. You'll lead a team focused on building Kraken's real-time streaming infrastructure and manage the architecture for high-volume data systems. A successful candidate will have over 8 years of experience in data engineering and at least 3 years in a leadership role. They should have strong skills in Kafka, Spark, and AWS, along with the ability to drive AI automation in data workflows. This role offers a dynamic and collaborative work environment with a focus on technical excellence.

Qualifications

• 8+ years in data engineering, platform engineering, or distributed systems.
• 3+ years managing engineering teams.
• Experience with real-time data processing at scale.

Responsibilities

• Lead and grow the data platform team.
• Own architecture and roadmap for data systems.
• Drive AI automation and intelligent workflows.

Skills

Data engineering

Platform engineering

Distributed systems

Cloud-native data infrastructure

AI/ML integration

Leadership

Tools

AWS

Spark

Kafka

Python

Java

Job description

Kraken's Data Platform team builds the real-time infrastructure that powers decision-making across one of the world's largest digital asset exchanges. 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.

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 serves 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

Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis.

Applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution.

We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.

We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments are designed to evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Assessment results are considered alongside other relevant information, such as experience and interviews, and are not the sole basis for any employment decision.

As an equal opportunity employer, we don’t tolerate discrimination or harassment of any kind. Whether that’s based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status or any other protected characteristic as outlined by federal, state or local laws.