Senior Software Engineer, Python, Data & Infrastructure
Northbeam
AirflowAWSBigQueryCloudDistributed SystemsDockerERPETLGoogle Cloud PlatformGraphQLKubernetesPython+2 more
Job Level
Senior
Tech Stack
AirflowBigQueryGoPythonSQL
About the role
- Canvas is at the forefront of revolutionizing the remodeling, architecture and interior design industry through LiDAR-enabled iPhone/iPad scans to capture precise 3D home representations and create editable as-built files.
- Define, own and execute Canvas’s data engineering and analytics strategy, modernizing the data stack and aligning with company goals.
- Act as a player/coach balancing hands-on technical contributions (designing data models and pipelines, building analytical reports) with leadership (building and managing the Data function).
- Establish and manage a centralized data platform with 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 cross-functional data needs.
- Lead adoption of modern tooling for ingestion (Airbyte, Fivetran), orchestration (Airflow, Prefect), transformation (dbt), and BI (Looker).
- Create processes to improve data consistency, reliability and monitoring; drive data governance and access control for compliance and security.
- 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 3D computer vision and AI.
Requirements
- Proven experience of building and leading Data teams in a high-growth startup or 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: Experience with establishing and managing data pipelines for ML/AI; knowledge of data privacy/PII management best practices and ISO 27001 / SOC 2; familiarity with CAD/BIM software industry and data formats.
