Head of Data

Responsibilities:

• Architect and maintain high-fidelity pipelines for internal product data and third-party integrations (e.g., regulatory data, banking systems).
• Define and manage canonical data models and metrics used across reporting, operations, and product workflows.
• Lead data quality efforts: validation, observability, lineage, and SLAs – building trust from ingestion to dashboard.
• Partner with engineering to influence schema design and logging at the source.
• Build and grow a team of high-agency data engineers and analysts focused on accuracy, reliability, and usability.
• Support product teams with experimentation and real-time decision infrastructure.
• Deliver self-service, well-documented data interfaces for internal teams and stakeholders.
• Set a high bar for precision, accountability, and long-term data stewardship.

What We’re Looking For:

• 8+ years of experience in data engineering, analytics engineering, or data platform roles, with at least 3 years leading teams.
• Deep expertise in data modeling, pipeline orchestration, and quality frameworks in a complex domain like venture.
• You think like a product owner: you measure success by what data unlocks, not what it looks like.
• Strong collaboration skills – can bridge technical, operational, and business domains.
• Track record of building from scratch and operating in high-ambiguity environments.
• Experience working closely with backend engineering; comfortable reviewing code, schemas, and logging patterns.
• High integrity and craftsmanship. You bring precision to naming conventions, documentation, and model definitions.
• Familiarity with financial systems, accounting data, or regulated environments is a plus.

How Success will be Measured in this Role:

• Data Quality & Trust: Significant improvement in data reliability and observability across critical systems, measured by reduction in data breakages, escalations, and reconciliation errors.
• Infrastructure Impact: Delivery of robust, scalable data pipelines and models that power operational, financial, and customer-facing workflows.
• Cross-Functional Enablement: High satisfaction from stakeholders (engineering, finance, product, ops) based on access to usable, well-modeled, and timely data.