Data Infrastructure

Responsibilities

• Own the data platform: Provide stewardship for our BigQuery and dbt infrastructure, improving reliability, performance, and developer experience.
• Enable teams: Build self-service capabilities and tooling that allow product, sales, marketing, and engineering teams to access and analyze data independently.
• Design data architecture: Define and implement our philosophy on data services, including pipeline patterns, data modeling standards, and integration approaches.
• Optimize infrastructure: Deeply understand GCP and BigQuery to architect cost-effective solutions that scale with our growth.
• Build collaboratively: Work with stakeholders across the company to understand data needs and build pipelines that serve multiple use cases.
• Establish best practices: Create documentation, templates, and standards that make it easy for teams to work with data correctly.
• Act as advisor to team who produce and consume data with the goal of ensuring best practices.
• Drive data quality: Implement monitoring, testing, and validation frameworks to ensure data reliability.

Requirements

• Strong foundation in data engineering principles and patterns; experience building scalable, maintainable data pipelines.
• Deep understanding of modern data warehouse architecture and analytical database design.
• Hands-on experience with BigQuery and GCP data services (or equivalent cloud data platforms).
• Proficiency with data transformation frameworks (dbt strongly preferred).
• Experience with data visualization tools (Hex preferred).
• Track record of balancing technical excellence with pragmatic cost optimization.
• Ability to translate business requirements into robust data solutions.
• Experience enabling non-technical teams to self-serve analytics.
• Strong communication skills and comfort working across organizational boundaries.

Preferred Experience

• Experience with workflow orchestration tools beyond GitHub Actions (Airflow, Prefect, Dagster, etc.).
• Knowledge of real-time data processing and streaming architectures.
• Background in data governance and quality frameworks.
• Experience with reverse ETL or operational analytics patterns.
• Familiarity with BI tools and semantic layers.
• Understanding of privacy and compliance considerations in data systems.

Compensation

• Target comp: $165,000 - $185,000