Head of Data Intelligence

The Role

You will build and operate a company-wide data intelligence function end to end: a business and investor observability platform, a customer health intelligence framework, a real-time signal detection system, and an AI agent layer that makes all of it accessible in plain language. Executive sponsorship, an organizational mandate, and the resources to build from the ground up.

The company processes identity and fraud signals at massive scale in real time, backed by consortium data that reveals patterns no single institution could see. This role builds the system that makes the company as smart about itself as it is about the identities it verifies.

What You Will Build

Business and Investor Intelligence. An authoritative data foundation with version-controlled, enforced metric definitions, so any investor or strategic question gets a verified answer in seconds with zero definitional conflicts across teams.

Customer Health Intelligence. A weekly scoring model across every active account spanning model freshness, use case breadth, volume trend against commitment, engagement quality, and configuration hygiene across 3,000+ product settings, with automated alerting and a structured intervention system for CS and Account Management.

Customer Signal Intelligence and Anomaly Detection. A real-time monitoring system that detects population-level behavioral anomalies, model drift, and emerging fraud attack signatures across the full customer base, delivering a structured incident brief to the right contact within 15 minutes of onset.

The AI Agent Layer. A natural language interface powered by AI agents sitting above all three pillars. Any employee should be able to ask a business question in Slack and get a verified, sourced answer in under 30 seconds. The multi-agent pipeline handles query understanding, retrieval, verification, and synthesis, with answer accuracy tracked as a first-class metric.

What We Are Looking For

You have built data platforms at scale and understand the full stack from ingestion through semantic layer to consumption. Agents and LLM-powered reasoning are core architectural instincts, not bolt-ons. You are as credible in a board prep session as in a technical design review. You know the hardest part is governance: getting a fast-moving organization to agree on canonical definitions and holding the line when the business wants to outpace the data model.

Required

12+ years of engineering experience, including significant time building large-scale data platforms, warehouses, and analytics infrastructure

5+ years leading engineering teams with company-wide scope

Track record building enterprise data platforms spanning transactional, CRM, and billing source systems

Deep fluency with modern data stack architecture: cloud data warehouses, data mesh and medallion patterns, semantic and metrics layers

Hands-on experience shipping AI agent systems: multi-agent orchestration, RAG pipelines, LLM-powered natural language interfaces

Strong instincts around statistical process control, anomaly detection, and real-time streaming architectures

Data governance depth: metrics catalogs, lineage, ownership models, version-controlled metric definitions

Experience translating board and investor questions into data architecture requirements

Excellent communication across technical and executive contexts

Preferred

Deep experience with AWS data services (S3, Glue, Athena, Redshift, Lake Formation, EMR, Kinesis, Lambda)

Modern data stack tooling (dbt, Airflow, Spark, Fivetran, Databricks, SageMaker Unified Studio, or similar)

Partnering with Finance and FP&A on revenue recognition, ARR/NRR analytics, and usage-based billing reconciliation

Supporting investor, board, or capital markets reporting in high-growth SaaS

Background in fintech, identity verification, fraud, or similarly high-stakes data domains

Building data platforms for regulated financial workflows (SOC 2, PCI, or similar)

Usage-based or consumption-based SaaS experience