Data Analytics AI Engineer

The world of digital assets is accelerating in speed, magnitude, and complexity, opening the door to new ways for leveraging the blockchain. Fireblocks’ platform and network provide the simplest and most secure way for companies to work with digital assets and it trusted by some of the largest financial institutions, banks, globally-recognized brands, and Web3 companies in the world, including BNY Mellon, BNP Paribas, ANZ Bank, Revolut, and thousands more.

Join our global Data team, which partners closely with Product, Engineering, and GTM stakeholders to power data-driven decision making and AI-driven products across Fireblocks. The team has evolved from providing deep analysis to becoming enablers, bringing the right data and AI tools so that Sales, Marketing, and Customer Operations can run analysis and forecasting. We operate in a fast-paced, high-growth environment where much of what we build is new, and we figure things out as we go.

• Design and build LLM-powered systems and data agents that directly improve internal capabilities, enabling GTM, Product, and Engineering teams to make faster, smarter, data-driven decisions at scale.
• Analyze large-scale datasets to surface strategic insights across the customer lifecycle and revenue funnel; define and track critical KPIs; and translate findings into actionable product and business recommendations.
• Evaluate and quantify the business impact of AI initiatives through rigorous experimentation, benchmarking, and model evaluation to continuously optimize LLM performance.
• Continuously monitor, refine, and evolve AI models and solutions, iterating on prompt engineering and deployment practices to keep pace with rapidly advancing capabilities.
• Ship and maintain production-grade analytics and AI applications through modern CI/CD pipelines (Git Lab/Git Hub), ensuring reliable, repeatable, and observable deployments.
• Manage and influence senior business stakeholders (including CRO-level), owning projects end-to-end and translating complex data and AI concepts into business outcomes.
• Leverage AI-assisted development tools (e.g., Cursor, Claude Code) to accelerate delivery speed across the team.

Required

• 5+ years of experience in AI/ML engineering or a combined data analytics and AI role.
• Strong SQL proficiency; solid Python experience is a strong plus.
• Hands‑on experience with LLMs, prompt engineering, fine‑tuning, and model evaluation pipelines.
• Strong analytical background: experience defining KPIs and communicating data‑driven recommendations to senior business stakeholders, with commercial acumen across Sales, Marketing, and revenue/pipeline.
• Experience with CI/CD pipelines using Git Lab or Git Hub for analytics or AI workloads.
• Demonstrated ability to own projects independently: scoping problems, navigating ambiguity, and managing stakeholder relationships without heavy oversight.
• Strong cross‑functional communication skills: able to present AI concepts and outcomes to both technical and non‑technical audiences, up to CRO level.
• Ability to work in our NYC office ~3 days in office weekly

Nice to Have

• Practical knowledge of Snowflake as a database agent platform, including MCP, database agents, and semantic views/YAMLs, highly valued given our current stack.
• Experience with dbt (dbt Cloud preferred) for analytics engineering workflows.
• Familiarity with the digital assets, fintech, or Web3 domain.

Within your first year, you have delivered AI solutions and data agents with clear business impact across GTM and Product, become a trusted partner for data‑driven AI initiatives across the org, and helped elevate the team's velocity and capabilities.

For employees hired to work from our NYC HQ, Fireblocks is required by law to include a reasonable estimate of the compensation range for this role. This range is specific to New York City and takes into consideration a wide range of factors that are reviewed when making a hiring decision, such as years of experience, skills, and other business needs.

It is not typical for a candidate to be hired at or near the top of the pay range and each compensation decision…