The Data Platform team collaborates with an experienced group of data scientists, engineers, and product managers to build highly available and scalable data infrastructure for TRM's products and services. As an Engineering Manager, you will be responsible for executing mission-critical systems and data services that analyze blockchain transaction activity at petabyte scale, and ultimately work to build a safer financial system for billions of people.

The impact you’ll have here:

• Lead and develop a team of talented, high-impact engineers, providing cultural, technical, and hands-on leadership.
• Provide regular coaching and technical mentorship to your immediate team as well as the broader engineering department.
• Take direct responsibility for the quality and delivery of the team's work, and manage the end-to-end availability and performance of critical services.
• Drive work based on goals and measurable outcomes that are accountable to KPIs.
• Encourage best practices in design and usability, creating scalable engines with thorough documentation and knowledge sharing.
• Resolve escalations arising from operations and work with various teams to solve production incidents through to resolution.
• Collaborate with management and product teams to plan, estimate, and prioritize roadmap objectives and identify upcoming opportunities and risks.
• Build highly reliable data services to integrate with dozens of blockchains.
• Develop ETL pipelines that transform and process petabytes of structured and unstructured data in real time.
• Design data models for optimal storage and retrieval to support sub-second latency for querying blockchain data.
• Deploy and monitor large database clusters that are performant and highly available.
• Work cross-functionally with data scientists, backend engineers and product managers to design and implement, and new data models to support TRM’s products.

What we’re looking for:

• Bachelor's degree (or equivalent) in Computer Science or a related field.
• 5+ years of experience building distributed system architecture, from whiteboard to production.
• Strong programming skills in Python, and SQL or SparkSQL.
• Versatility. Experience across the entire spectrum of data engineering, including:
  • Data stores (e.g., ClickHouse, ElasticSearch, Postgres, Redis, and Neo4j)
  • Data pipeline and workflow orchestration tools (e.g., Airflow, DBT, Luigi, Azkaban, Storm)
  • Data processing technologies and streaming workflows (e.g., Spark, Kafka, Flink)
  • Deployment and monitoring infrastructure in public cloud platforms (e.g., Docker, Terraform, Kubernetes, Datadog)
  • Loading, querying, and transforming large data sets