Engineering Manager, Data Cloud

We're looking for an Engineering Manager who leads through service, not authority. You'll partner with a Staff Data Engineer who drives the technical vision and a team of data engineers who are building and maintaining data pipelines, as well as the data cloud infrastructure. Your job is to create the conditions where every engineer on this team does the best work of their career, by removing blockers, coaching growth, driving crisp execution, and building a culture where curiosity, engineering excellence, and intelligent use of AI are the norm.

- 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

- 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.

- Knowledge of the modern data stack: Spark, Databricks, Kafka, Delta Lake/Iceberg, some experience with Flink and/or StarRocks would be appreciated.

- 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

- Infrastructure: Terraform, AWS (S3, EC2, EKS, IAM), Kubernetes, Helm