Data Analytics AI Engineer

Fireblocks•New York, NY

1d•$156,000 - $204,000•Hybrid

About The Position

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.

Requirements

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 GitLab or GitHub 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 Haves

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.

Responsibilities

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 (GitLab/GitHub), 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.