SaaS Analytics Engineer - dbt, Snowflake & Metrics

Splashtop Inc.; Cupertino (CA); On-site; USD 110,000 - 120,000; Full time

Benefits offered by this job

Base salary 110k-120k USD/year

Learning and development

Collaborative culture

Global team

Job summary

Splashtop Inc. in Cupertino, CA, is seeking an Analytics Engineer to join the data team and scale our data infrastructure.

You’ll design reliable data models and pipelines using dbt and Snowflake, partnering with product, GTM, and finance to deliver trusted analytics for daily decision-making. The role suits someone with 3–5 years in analytics or data engineering in SaaS, strong SQL, and experience with SaaS metrics (ARR, churn, retention).

Qualifications

• 3–5 years in analytics engineering or data engineering in SaaS.
• Production experience with dbt for data transformation and modeling is a must.
• Strong SQL skills and ability to write efficient queries.
• Familiarity with SaaS metrics (ARR, churn, retention) and data warehousing.

Responsibilities

• Design, build, and maintain scalable data models and pipelines using dbt and Snowflake.
• Partner with data engineers, analysts, and stakeholders to translate data into trusted datasets.
• Own end-to-end lifecycle of data models: development, testing, documentation, deployment, monitoring.
• Establish data modeling best practices, naming conventions, and testing standards.
• Build core SaaS metrics as reusable, auditable data models; improve quality and observability.
• M optimize Snowflake performance and cost; collaborate with BI teams.

Skills

SQL

dbt

Snowflake

Dimensional modeling

ELT/ETL

Git / CI/CD

SaaS metrics

communication

Tools

dbt

Snowflake

Looker/Tableau/Mode/Sigma

Job description

Splashtop Inc. in Cupertino, CA, is seeking an Analytics Engineer to join the data team and scale our data infrastructure.

You’ll design reliable data models and pipelines using dbt and Snowflake, partnering with product, GTM, and finance to deliver trusted analytics for daily decision-making. The role suits someone with 3–5 years in analytics or data engineering in SaaS, strong SQL, and experience with SaaS metrics (ARR, churn, retention).