Senior Data Analyst, Enterprise
New
1
1PasswordB2B SaaS
Remote (United States | Canada); This is a remote opportunity within Canada and the US.Full-TimeSenior
SalaryUSA-based roles only: The annual base salary for this role is between $122,000 USD and $171,000 USD; Canada-based roles only: The annual base salary for this role is between $105,000 CAD and $147,000 CAD, plus an equity grant and, where applicable, participation in our incentive programs.
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Job Details
- Experience
- 5+ years in analytics within complex B2B SaaS environments
- Required Skills
- SQLSalesforceSnowflakeA/B testingdbtDatabricks
Requirements
- Bring 5+ years of analytics experience in complex B2B SaaS environments; product-led or self-serve experience is preferred.
- Have strong product-led growth and customer lifecycle expertise, including activation, PQLs, cohort analysis, retention, churn, expansion, and growth modeling.
- Have experience partnering with Customer Success, Product, and Growth stakeholders.
- Translate open-ended business questions into analytical frameworks.
- Synthesize complex analysis into clear recommendations and communicate them to senior leaders.
- Have experience with experimentation, including A/B testing, statistical modeling, or predictive techniques such as churn or propensity modeling.
- Use advanced SQL and work with large-scale analytical datasets, including product usage and event data.
- Have hands-on experience with modern AI/BI tools such as Hex, Omni, Claude, Dust, or similar.
- Be comfortable with cloud data ecosystems such as Snowflake, Databricks, and dbt.
- Have experience with GTM and customer platforms such as Salesforce and a customer success platform such as Gainsight or similar.
- Product analytics tools such as Amplitude or Segment are a plus.
- B2C or hybrid B2B/B2C experience is a plus.
Responsibilities
- Analyze customer journeys from sign-up through activation, engagement, conversion, expansion, and renewal.
- Identify the drivers of adoption, retention, and expansion across digital and scaled customer success programs.
- Define how digital and scaled success programs are measured.
- Size opportunities and recommend where the business should invest next.
- Translate complex analysis into clear recommendations for leadership.
- Partner with Customer Success, Product, Growth, RevOps, and Data Engineering.
- Contribute to the data foundations and analytical standards supporting Customer Success and AI capabilities.
- Use customer analytics to support forecasting, anomaly detection, and insight generation.
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