Data Lead

Canvas is at a pivotal moment in scaling its customer base, operations, and data-driven decision-making. We are seeking a Data Lead to own and execute our data engineering and analytics strategy, modernize our data stack, and establish a foundation for scalable, trustworthy, and self-service analytics across the company.

This role will act as a Player/Coach, balancing hands-on technical contributions with leadership responsibilities. The Data Lead will belong to the Engineering Leadership Team, will report directly to VP Engineering, and will play a central role in building a high-performing Data team, implementing modern data engineering and analytics practices, and ensuring that data becomes a strategic asset driving business impact across all functions.

This is a unique opportunity to shape the future of data at Canvas, designing and implementing scalable pipelines, frameworks, and processes while also serving as the primary partner to business and product teams. The ideal candidate will bring a blend of technical depth, strong analytical acumen, and leadership experience to guide both infrastructure and business intelligence efforts.

Key Responsibilities:

• Define, own and execute Canvas’s data engineering and analytics strategy, ensuring alignment with company goals.
• Act as both a hands-on contributor (designing data models and pipelines, defining governance processes, building analytical reports) and a team leader (building out and managing the Data function).
• Establish and manage a centralized data platform, implementing best practices around data modeling, transformation, version control, testing, and observability.
• Partner with stakeholders from Go-to-Market, Product, Engineering, Operations, and Finance to serve the data needs across various functions of the business.
• Lead the adoption of modern tooling for data ingestion (e.g. Airbyte, Fivetran), orchestration (e.g. Airflow, Prefect), transformation (e.g. dbt), and BI (e.g. Looker).
• Create processes to improve data consistency, reliability and monitoring, ensuring issues are caught proactively rather than by business teams.
  Drive data governance and access control, ensuring compliance, trust, and security across the organization.
• Enable a self-service analytics culture by designing semantic layers, improving BI usability, and training business stakeholders.
• Manage roadmaps, priorities, and SLAs, balancing short-term needs with long-term scalability.
• Help establish efficient machine learning data pipelines required for solving core tech problems in the fields of 3D computer vision and AI.
• Be part of the company’s Engineering Leadership Team, thus influencing engineering processes and culture across all teams.

Preferred Qualifications:

• Proven experience of building and leading Data teams in a high-growth startup or in a fast-scaling tech environment, including both technical and people management.
• Strong technical background in data engineering and analytics.
• Hands-on expertise with the modern data engineering and analytics tech stack:
  • Data modeling and transformation (e.g. dbt)
  • Data warehouse technologies (e.g. BigQuery, Snowflake)
  • Orchestration tools like Airflow or Prefect
  • Data modeling frameworks (e.g. Kimball, 3NF)
  • ELT pipelines and third-party data ingestion tools (e.g. Fivetran, Airbyte, Stitch)
  • BI tools (e.g. Looker)
• Proficiency with SQL and Python
• Solid understanding of general software engineering and architecture practices, including git-based development workflows.
• Knowledge and hands-on experience with product and marketing analytics: Segment, Mixpanel, AppsFlyer, CDPs, attribution, CRM/ads integration, etc.
• Demonstrated ability to translate business needs into technical solutions and influence stakeholders at all levels.
• Excellent communication and leadership skills, with a track record of mentoring, collaboration, and driving cultural adoption of data practices.
• Fluent English, both spoken and written.

Bonus Qualifications:

• Experience with establishing and managing data pipelines for ML/AI.
• Knowledge of data privacy/PII management best practices, experience with ISO 27001 / SOC 2 certifications.
• Familiarity with the CAD/BIM software industry and data formats.