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Analytics Engineer

Splashtop LogoSplashtop
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Location

Cupertino, CA (On-site)

Posted

Today

We're looking for an experienced Analytics Engineer to join our data team and help scale the infrastructure that powers decision-making across the business. You'll sit at the intersection of data engineering and analytics, building reliable, well-tested data models that product, GTM, and finance teams depend on every day. This role is ideal for someone who has spent 3-5 years working in fast-moving SaaS environments and understands the unique data challenges that come with subscription businesses: usage metrics, churn and retention, ARR reporting, and product analytics at scale.

What you'll do

  • Design, build, and maintain scalable data models and transformation pipelines using dbt and Snowflake to support reporting, analytics, and data science use cases.
  • Partner with data engineers, analysts, and business stakeholders to translate raw event and operational data into trusted, well-documented datasets.
  • Own the full lifecycle of data models: development, testing, documentation, deployment, and monitoring.
  • Establish and enforce data modeling best practices, including naming conventions, testing standards, and style guides across the dbt project.
  • Build and maintain core SaaS metrics (ARR, churn, expansion/contraction, cohort retention, product usage) as reusable, auditable data models.
  • Improve data quality and observability through automated testing, alerting, and documentation.
  • Optimize Snowflake performance and cost through efficient warehouse usage, query design, and data modeling patterns.
  • Collaborate with BI teams and self-serve analytics users to ensure models are intuitive, performant, and aligned with business logic.
  • Participate in architectural decisions around the modern data stack (ingestion, orchestration, transformation, and BI layers).
  • Build and maintain a semantic layer to centralize all business metrics.

What you'll bring

  • 3-5 years of experience in analytics engineering, data engineering, or a similar data-focused role, with significant time spent in a SaaS or subscription-based business.
  • Hands-on, production experience with dbt for data transformation and modeling — this is a must-have, not a nice-to-have.
  • Strong SQL skills, with the ability to write complex, efficient, and maintainable queries.
  • Solid understanding of dimensional modeling, ELT/ETL design patterns, and data warehousing concepts.
  • Experience working with SaaS metrics and business models (ARR, churn, retention, cohort analysis, usage-based billing, etc.).
  • Familiarity with version control (Git) and CI/CD practices as applied to data pipelines.
  • Excellent communication skills and the ability to work cross-functionally with both technical and non-technical stakeholders.
  • Extremely detail-oriented, able to look at each row of a model to ensure data quality and accuracy.

Nice to have

  • Experience with BI tools such as Looker, Tableau, Mode, or Sigma.
  • Familiarity with event tracking and CDP tools.
  • Experience with Python for data processing, automation, or testing.
  • Background in a high-growth or scaling SaaS company.
  • Experience with data orchestration, data governance, cataloging, or lineage tools.
  • Ability to think architecturally to produce efficient models and pipelines.
  • Experience with dbt CLI and setting up MCP servers. Familiarity with advanced Git features and usage (GitHub actions, etc.)

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