Engineering Manager, Data Cloud
Chainalysis is the blockchain data platform.
About Chainalysis
Chainalysis offers a blockchain data platform that utilizes sophisticated machine learning and proprietary architecture to handle extensive clustering heuristics, ingest data at scale, and ensure high data accuracy. The platform supports new blockchains and standard tokens automatically, providing comprehensive industry coverage. It simplifies tracing fund flows through complex transactions like bridges, mixers, and DEX swaps. The data provided by Chainalysis is court-admissible and has been instrumental in legal actions. The company provides global, 24/7 support with localized guidance and expertise in various threat typologies and advanced investigative techniques. Through its R&D initiative, Chainalysis Labs, the company continues to innovate and introduce new, unique features and capabilities in blockchain intelligence.
Skills
About the Role
You'll lead, coach, and develop a team of six engineers spanning streaming, data lakehouse, serving layer, and platform infrastructure, partnering closely with a Staff Data Engineer who drives the technical vision. You'll serve the team by removing obstacles, shielding them from organizational noise, and making sure they have what they need to ship. You'll own the quarterly plan and sprint-level execution, translating OKRs into milestones with clear owners, timelines, and success criteria. You'll coach each engineer toward their next level with specific development plans, timely feedback, and active promotion sponsorship. You'll champion engineering best practices like design reviews, ADRs, blameless post-mortems, automated testing, and data quality, and you'll manage the on-call rotation and incident response process so reactive work doesn't consume the team's capacity to build. You'll build enough understanding of the data cloud architecture to ask sharp questions and have credible conversations with stakeholders, foster a culture of curiosity and continuous learning, and hire exceptional talent to grow the team. You'll also drive AI adoption across the team's engineering workflows, acting as a role model for integrating AI tools into daily development, code review, documentation, and debugging.
Requirements
- Managed a team of 5–10 engineers building data infrastructure, data platforms, or backend systems at scale with a servant leadership philosophy
- A software or data engineering background with the ability to read a Terraform plan and follow a streaming architecture discussion
- A track record of developing people, including coaching engineers to promotion and giving hard feedback that led to growth
- Strong execution habits including creating and maintaining project timelines
- The ability to communicate clearly to technical and non-technical stakeholders
- Collaborative instincts and experience working cross-functionally with Product, engineering teams, and leadership
- An interest in or curiosity about cryptocurrency and blockchain technology
- A proactive mindset toward AI-assisted engineering with hands-on use of tools like Copilot, Claude, ChatGPT, or Cursor
- Knowledge of the modern data stack such as Spark, Databricks, Kafka, Delta Lake/Iceberg, Flink, and/or StarRocks
- Cloud cost optimization experience and FinOps practices
- A background in blockchain, fintech, or other data-intensive domains
- Experience driving AI adoption within an engineering team
- Hands-on experience with AI coding assistants such as Claude Code, GitHub Copilot, or Cursor
Responsibilities
- Lead, coach, and develop a team of 6 engineers spanning streaming, data lakehouse, serving layer, and platform infrastructure
- Serve the team by removing obstacles, shielding them from organizational noise, and ensuring they have what they need to ship
- Own the quarterly plan and sprint-level execution, translating OKRs into milestones with clear owners, timelines, and success criteria
- Coach each engineer toward their next level with specific plans, timely feedback, and active promotion sponsorship
- Champion engineering best practices including design reviews, ADRs, blameless post-mortems, automated testing, and data quality
- Manage the on-call rotation and incident response process
- Build an understanding of the data cloud architecture to ask better questions and anticipate risks
- Foster a culture of curiosity and continuous learning
- Hire exceptional talent to grow the team with a focus on diversity and raising the bar
- Drive AI adoption across the team's engineering workflows