Director, Data Engineering

SQUIRE is a business management system designed for barbers, shop owners, and their communities. The Director, Data Engineering will build and lead the Data organization through a player-coach model, owning data architecture, technical strategy, team development, and hands-on delivery across Data Engineering, Machine Learning Engineering, and Analytics. The role will establish reliable, scalable data foundations that support reporting, automation, and AI initiatives.

Responsibilities:

• Design, build, and debug the highest-impact pipelines, connecting our business systems, including CRM, billing, and internal platforms to our Snowflake warehouse, in both directions
• Own the data strategy and architecture across a multi-year horizon, while maintaining deep, hands-on familiarity with the systems and their edge cases
• Establish technical direction and standards for pipeline design, data modeling, and data quality across the org; personally build and ship the highest-risk or most ambiguous work
• Build, structure, and lead a growing Data organization of 5+ across Data Engineering, ML Engineering, and Analytics
• Own headcount planning, hiring strategy, and budget for the Data organization
• Partner with the Chief Product and Technology Officer and cross-functional leaders to translate company strategy into data contracts and platform investments
• Lead audits of existing integrations and automations, and determine what should be replaced and how that work should be prioritized
• Champion AI and automation enablement company-wide, ensuring agents and LLM use cases are built on a trusted data foundation, while remaining hands-on in building select enablement capabilities

Requirements:

• 8+ years in data or software engineering, including 4+ years managing engineers
• Experience building and leading a multidisciplinary data organization (data, ML, analytics, software) of 5+ people
• Advanced SQL and Python skills, along with strong MDM fundamentals, with the technical depth to lead by example
• Hands-on experience with Snowflake (or equivalent), dbt, and an orchestration tool such as Airflow, Dagster, or Prefect, beyond just an architectural understanding
• Direct experience setting AI and LLM enablement strategy and building with LLM tooling
• Comfortable operating at Director scope (owning budgets, advocating for roadmaps, and translating technical tradeoffs for non-technical stakeholders) while remaining directly involved in technical execution
• Experience building AI agents with LLMs (wired models into real systems with tool use, retrieval, and orchestration) and with the ability to guide others doing similar work
• Backend engineering experience (ideally in JS/TS) with sufficient depth to effectively evaluate architecture
• Strong ML and MLOps knowledge, with the ability to set direction, assess technical tradeoffs, and support ML Engineering Leads
• Experience with Salesforce, Stripe, Gong, or reverse ETL (Polytomic), and data governance / PII handling at a company-wide policy level