Lead Data Engineer, Galaxy

• You're a senior, hands-on data engineer with 8+ years of experience designing, building, and operating production data platforms at scale — and you're ready to lead
• You set the technical direction for data infrastructure, mentor other engineers, and raise the bar for engineering quality across the team
• You're fluent in Python or Java and deeply experienced across the modern data stack — AWS, Databricks/Spark, dbt, and Airflow
• You care about data quality, write clean and well-tested code, and own systems end-to-end from architecture through production
• You're curious about crypto and energized by working at the intersection of finance and emerging technology
• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• 8+ years of professional experience in data engineering or distributed systems, including time spent in a technical leadership or staff-level role
• Expert hands-on coding experience in Python, Java, or similar languages
• Proven track record of leading the design and delivery of large-scale data platforms in production
• Experience building and operating both batch and streaming data pipelines
• Deep experience with AWS, Databricks/Spark, and modern data architectures (lakehouse, streaming, real-time analytics)
• Hands-on experience with dbt for transformations and Airflow (or similar) for orchestration
• Solid understanding of data modeling, data quality, and production data systems
• Experience working with relational and non-relational databases (e.g., Postgres, SQL Server)
• Strong working knowledge of CI/CD, testing frameworks, and monitoring/observability tools
• Experience mentoring engineers and influencing technical direction across teams

Desirable

• Set the technical vision and roadmap for Galaxy's data platform, partnering across business lines (Trading, Custody, Asset Management, Mining) to deliver reliable, scalable infrastructure
• Architect, build, and operate scalable batch and streaming data pipelines, owning them from design through production
• Develop and operate high-throughput, low-latency data processing systems
• Work within the Databricks and Spark ecosystem to support large-scale data workloads
• Build and maintain cloud-native data solutions on AWS
• Leverage dbt for transformation, data modeling, testing, and documentation
• Use Airflow (or similar orchestration tools) to manage and schedule data workflows
• Write clean, efficient, and well-tested code, following best practices in software engineering
• Mentor and develop other engineers, lead code reviews, and raise the bar for engineering quality across the team
• Drive technical design discussions and decisions across teams, and continuously improve system performance, scalability, and reliability
• Own production reliability — lead incident response, root-cause analysis, and long-term remediation
• Partner with product, analytics, and infrastructure teams to deliver impactful data solutions aligned to business priorities