Principal Analytics Engineer – Data Architect

Role Overview

• Lead the end-to-end system design and architecture for large, complex analytics systems, optimizing data transformations from our data lake in Snowflake into analytical data products (DWs).
• Define and drive the adoption of design standards, architectural principles, and data modeling methodologies (including Data Vault (DV 2.0) and dimensional modeling) across the organization.
• Propose and drive strategic initiatives and coordinate the technical roadmap for analytical systems across the engineering organization partnering closely with the Big Data Platforms (BDP) team.
• Serve as the data architect for critical SaaS business domains, including Go-To-Market, Marketing, Customer Experience, and Finance, translating ambiguous business problems into scalable technical analytical solutions.
• Develop and implement the organization's data strategy for data quality, observability, and testing, raising the bar for engineering quality.
• Architect and guide teams to build and maintain a governed, robust, reliable, and scalable analytics system leveraging Gen AI capabilities including AI-assisted code development practices.
• Act as a trusted advisor to BI analysts, data scientists, and business leaders, educating and advising leadership on technical data topics and data accessibility strategies.
• Foster data democratization via activation of data on critical platforms like Smartsheet Platform, Amplitude, ThoughtSpot, Gainsight & Salesforce.
• Actively mentor analytics engineers and provide mentorship across disciplines, leading forums such as guilds, design reviews, and engineering forums.
• Collaborate with cross-functional teams to assess data privacy needs and define data masking strategies aligned with industry compliance standards (e.g., GDPR, HIPAA, CCPA).

Requirements

• 12+ years of experience in advanced data modeling, data architecture, and data warehousing.
• Deep expertise and 10+ years of hands-on experience with Data Vault (DV 2.0) and Dimensional Modeling.
• Expert-level knowledge of modern data stacks, including Snowflake, dbt, and data orchestration tools (e.g., Airflow, GitLab pipelines).
• Proven experience architecting and building analytical products for SaaS companies in GTM, Marketing, Customer Experience, and Financial data domains.
• Comprehensive understanding of data topologies (e.g., data mesh, hub/spoke) and data modeling methodologies (e.g., Data Vault, Kimball, Inmon).
• Demonstrated ability to drive clarity in ambiguous environments where the strategy may not be defined.
• Proven experience with building and maintaining business analytics layers and semantic layers to enable self-service for non-technical end-users.
• Strong production experience supporting and maintaining analytics/data warehouse solutions with mission-critical datasets.
• Exceptional communication skills, with experience educating and advising senior leadership and mentoring teams across disciplines.
• Strong knowledge of data governance principles, CI/CD practices (e.g., GitLab), self-serve analytics tools (e.g., Amplitude, ThoughtSpot), and cloud architectures (AWS preferred).

Tech Stack

• Airflow
• AWS
• Cloud
• Vault
• Go

Benefits

• Equal Opportunity Employer
• inclusive environment