All-in-one barbershop management platform
Director, Data Engineering
Location
New York
Posted
3 days ago
Salary
$200K - $230K / year
Seniority
Lead
Job Description
Director, Data Engineering
SQUIRE
• 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
Job 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
Benefits
- Base Salary ($200,000 - $230,000) + Bonus (15%)
- New hire stock grant
- 100% employer-paid medical/dental/vision insurance for you and your dependents
- 401K plan with company contribution
- Generous PTO and Parental Leave policies
Related Guides
Related Categories
Related Job Pages
More Data Engineer Jobs
• Conduct comprehensive gap analysis and data mapping across 80+ ERP systems to identify integration challenges, data inconsistencies, and transformation requirements. • Design and develop standardized ETL pipelines and data transformation processes for migrating enterprise data into Microsoft Fabric. • Build robust data quality frameworks and validation rules to ensure data readiness for AI and analytics workloads. • Lead hands-on implementation of ETL standards and best practices, establishing repeatable patterns and automation scripts for multi-source integrations. • Develop and optimize data models that support seamless transformation from fragmented ERP sources into a unified, AI-ready data architecture. • Lead the pilot implementation phase, testing and refining standards against real ERP data before full-scale rollout. • Mentor and guide team members on ETL development, data transformation techniques, and Fabric-specific engineering practices. • Collaborate with stakeholders to document data lineage, transformation logic, and integration patterns for knowledge transfer and governance. • Troubleshoot data inconsistencies and implement corrective measures to maintain data integrity throughout the pipeline.
• Conduct comprehensive gap analysis and data mapping across 80+ ERP systems to identify integration challenges, data inconsistencies, and transformation requirements. • Design and develop standardized ETL pipelines and data transformation processes for migrating enterprise data into Microsoft Fabric. • Build robust data quality frameworks and validation rules to ensure data readiness for AI and analytics workloads. • Lead hands-on implementation of ETL standards and best practices, establishing repeatable patterns and automation scripts for multi-source integrations. • Develop and optimize data models that support seamless transformation from fragmented ERP sources into a unified, AI-ready data architecture. • Lead the pilot implementation phase, testing and refining standards against real ERP data before full-scale rollout. • Mentor and guide team members on ETL development, data transformation techniques, and Fabric-specific engineering practices. • Collaborate with stakeholders to document data lineage, transformation logic, and integration patterns for knowledge transfer and governance. • Troubleshoot data inconsistencies and implement corrective measures to maintain data integrity throughout the pipeline.
• Conduct comprehensive gap analysis and data mapping across 80+ ERP systems to identify integration challenges, data inconsistencies, and transformation requirements. • Design and develop standardized ETL pipelines and data transformation processes for migrating enterprise data into Microsoft Fabric. • Build robust data quality frameworks and validation rules to ensure data readiness for AI and analytics workloads. • Lead hands-on implementation of ETL standards and best practices, establishing repeatable patterns and automation scripts for multi-source integrations. • Develop and optimize data models that support seamless transformation from fragmented ERP sources into a unified, AI-ready data architecture. • Lead the pilot implementation phase, testing and refining standards against real ERP data before full-scale rollout. • Mentor and guide team members on ETL development, data transformation techniques, and Fabric-specific engineering practices. • Collaborate with stakeholders to document data lineage, transformation logic, and integration patterns for knowledge transfer and governance. • Troubleshoot data inconsistencies and implement corrective measures to maintain data integrity throughout the pipeline.
• Design and evolve data platforms using Lakehouse architecture • Develop scalable pipelines • Define architecture, governance, and performance standards with Databricks, Delta Lake, and PySpark • Implement solutions on Databricks and Delta Lake • Support technical teams and document solutions
