Principal Analytics Engineer (data Architect) - Hybrid in Bangalore

Smartsheet Smartsheet · Seattle · India · Business Intelligence & Ops

Smartsheet is seeking a Principal Analytics Engineer (Data Architect) to lead the end-to-end system design and architecture for large, complex analytics systems, optimizing data transformations from their data lake in Snowflake into analytical data products (DWs). This role will define and drive the adoption of design standards, architectural principles, and data modeling methodologies, and serve as the data architect for critical SaaS business domains. The role will also develop and implement the organization's data strategy for data quality, observability, and testing, and architect systems leveraging Gen AI capabilities including AI-assisted code development practices. The position requires 12+ years of experience in advanced data modeling, data architecture, and data warehousing, with deep expertise in Data Vault (DV 2.0) and Dimensional Modeling, and hands-on experience with modern data stacks including Snowflake and dbt.

What you'd actually do

  1. 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).
  2. 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.
  3. 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.
  4. 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.
  5. Develop and implement the organization's data strategy for data quality, observability, and testing, raising the bar for engineering quality.

Skills

Required

  • advanced data modeling
  • data architecture
  • data warehousing
  • Data Vault (DV 2.0)
  • Dimensional Modeling
  • Snowflake
  • dbt
  • data orchestration tools (e.g., Airflow, GitLab pipelines)
  • SaaS data domains (GTM, Marketing, Customer Experience, Finance)
  • data topologies
  • data governance principles
  • CI/CD practices (e.g., GitLab)
  • cloud architectures (AWS preferred)

Nice to have

  • Amplitude
  • ThoughtSpot
  • Gainsight
  • Salesforce
  • GDPR
  • HIPAA
  • CCPA

What the JD emphasized

  • end-to-end system design and architecture
  • architecting data solutions for SaaS companies
  • data modeling methodologies
  • data strategy
  • data quality, observability, and testing
  • governed, robust, reliable, and scalable analytics system
  • AI-assisted code development practices
  • advanced data modeling, data architecture, and data warehousing
  • hands-on experience
  • Data Vault (DV 2.0) and Dimensional Modeling
  • modern data stacks
  • data topologies
  • data modeling methodologies
  • building and maintaining business analytics layers and semantic layers
  • supporting and maintaining analytics/data warehouse solutions
  • data governance principles