Data Platform Engineering Manager

Job Description

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

RESPONSIBILITES

• Lead and grow a team of senior data platform engineers building our client’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 our client’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

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