Engineering Manager, Data Cloud

Chainalysis

23h

About The Position

The engineering team at Chainalysis is dedicated to solving complex technical challenges and building products that foster trust in cryptocurrencies. This global organization, with teams across the UK, Denmark, Canada, and the USA, focuses on creating a flexible platform to adapt to the rapidly changing industry. The Data Cloud team specifically develops and operates real-time streaming pipelines (Apache Flink), a data lakehouse (Databricks), and cloud infrastructure that enables the world to understand blockchain data. Their pipelines process billions of records daily, serving major institutions in the crypto space. This small team of 6 engineers manages petabyte-scale infrastructure. Chainalysis is seeking an Engineering Manager who leads through service, partnering with a Staff Data Engineer and a team of data engineers. The manager's primary role is to create an environment where every engineer can excel, by removing blockers, coaching growth, ensuring crisp execution, and cultivating a culture of curiosity, engineering excellence, and intelligent AI utilization.

Requirements

Managed a team of 5–10 engineers building data infrastructure, data platforms, or backend systems at scale — with a genuine servant leadership philosophy
A software or data engineering background — you've been a hands-on engineer and can read a Terraform plan, follow a streaming architecture discussion, and ask meaningful questions in a design review
A track record of developing people: coaching engineers to promotion, giving hard feedback that led to growth, and building teams where retention is high because people feel valued and challenged
Strong execution habits: you create and maintain project timelines, know when things are off track before your team tells you.
The ability to communicate clearly — you can explain a technical decision to a VP, write a concise incident summary, and draft a quarterly plan for your team
Collaborative instincts and experience working cross-functionally with Product, other engineering teams, and leadership in a fast-moving environment
An interest in or curiosity about cryptocurrency and blockchain technology — we can help you learn, but the curiosity has to be genuine
A proactive mindset toward AI-assisted engineering — you should already be using AI tools (Copilot, Claude, ChatGPT, Cursor, or similar) in your own work and have opinions about how they change engineering workflows, code quality, and team productivity. We're looking for someone who sees AI as a multiplier.

Nice To Haves

Knowledge of the modern data stack: Spark, Databricks, Kafka, Delta Lake/Iceberg, some experience with Flink and/or StarRocks would be appreciated.
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 — setting expectations, measuring impact, removing barriers to adoption, and evolving workflows as tools mature
Hands-on experience with AI coding assistants (Claude Code, GitHub Copilot, Cursor) and an understanding of where they accelerate development vs. where human judgment is irreplaceable

Responsibilities

Lead, coach, and develop a team of 6 engineers spanning streaming, data lakehouse, serving layer, and platform infrastructure — with genuine curiosity about each domain
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: translate OKRs into milestones with clear owners, timelines, and success criteria — and keep them updated without being asked
Coach each engineer toward their next level, with specific plans, timely feedback, and active promotion sponsorship when the work is done
Champion engineering best practices: design reviews before major changes, ADRs for architectural decisions, blameless post-mortems, automated testing, and data quality as a first-class citizen in every pipeline
Manage the on-call rotation and incident response process so that reactive work doesn't consume the team's capacity to build
Build an understanding of the data cloud architecture — not to design it, but to ask better questions, anticipate risks, and have credible conversations with stakeholders
Foster a culture of curiosity and continuous learning, where engineers explore new technologies, share knowledge, and question assumptions
Hire exceptional talent to grow the team with a focus on diversity, raising the bar, and complementing existing strengths
Drive AI adoption across the team's engineering workflows — the team has a mandate for AI adoption, and you'll be expected to be a role model, to champion this, remove friction, and help engineers integrate AI tools into their daily development, code review, documentation, and debugging practices