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Salary: USD 200,000 - 230,000 / annual
Squire is a business management platform serving barbers and shop owners globally, trusted by 30,000+ barbers across 5,000+ shops. The company is building its data organization to power company-wide reporting, automation, and AI capabilities.
You'll join as Director of Data Engineering, operating in a player-coach model: approximately 50% hands-on technical work and 50% leadership, strategy, and team development. You'll own Squire's long-term data architecture and lead a growing team spanning Data Engineering, Machine Learning Engineering, and Analytics.
Key responsibilities include:
- Design and build high-impact data pipelines connecting business systems (CRM, billing, internal platforms) to Snowflake
- Own data strategy and architecture across a multi-year horizon with deep hands-on familiarity
- Establish technical direction and standards for pipeline design, data modeling, and data quality
- Build, structure, and lead a Data organization of 5+ people; own headcount planning, hiring, and budget
- Partner with the Chief Product and Technology Officer to translate company strategy into data contracts and platform investments
- Lead audits of existing integrations and automations; determine priorities for replacement and modernization
- Champion AI and LLM enablement company-wide, ensuring agents and use cases are built on trusted data foundations
- Remain hands-on in building select high-risk or ambiguous work and select enablement capabilities
Required experience:
- 8+ years in data or software engineering, including 4+ years managing engineers
- Experience building and leading multidisciplinary data organizations (data, ML, analytics, software) of 5+ people
- Advanced SQL and Python; strong master data management fundamentals
- Hands-on experience with Snowflake, dbt, and orchestration tools (Airflow, Dagster, Prefect)
- Direct experience setting AI/LLM enablement strategy and building with LLM tooling
- Comfortable operating at Director scope (budgets, roadmaps, translating technical tradeoffs) while remaining hands-on
Nice-to-haves include experience building AI agents with LLMs, backend engineering (JS/TS), ML/MLOps expertise, and familiarity with Salesforce, Stripe, Gong, reverse ETL, or data governance at scale.
Reports to the Chief Product and Technology Officer. Full-time, remote (US or Canada).
