Senior Data Analyst
New
V
Veeam SoftwareSaaS
Remote, United StatesFull-TimeSenior
Salary121400 - 310900 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- SQLSnowflakeTableauRedshiftDatabricks
Requirements
- 5+ years of experience in a data analyst, business intelligence, or analytics engineering role, ideally within a SaaS or subscription-revenue environment.
- Advanced SQL skills. You write efficient, well-structured queries and are comfortable working with large, complex datasets.
- Tableau expertise. You've built polished, production-grade dashboards from scratch — not just modified existing views. You understand calculated fields, LOD expressions, performance optimization, and publishing/governance workflows.
- Executive communication skills. You're comfortable presenting to senior leaders, fielding tough questions, and distilling complex data into clear narratives.
- Strong business acumen. You understand corporate performance metrics and SaaS fundamentals — ARR, MRR, NRR, churn, expansion, contraction — and how they connect to the broader business.
- Self-directed and proactive. You take ownership of your domain, anticipate stakeholder needs, and don't wait to be told what to analyze.
- Strong critical thinking and intellectual curiosity.
- Experience with modern cloud data platforms (e.g., Databricks, Snowflake, Redshift) is a strong plus.
Responsibilities
- Own the Corporate Dashboard Portfolio: Design, build, and maintain Tableau dashboards spanning key corporate metrics, with a primary focus on ARR, incremental ARR, net revenue retention (NRR), acquisition, upsell, downsell, and churn.
- Deliver Analytical Insights: Go beyond reporting — proactively surface trends, anomalies, and opportunities across corporate and revenue data and present findings to senior leadership.
- Partner with Stakeholders: Serve as the primary analytics point of contact for executive and cross-functional teams, translating business questions into data-driven answers.
- Write and Optimize SQL: Query large-scale databases to extract, transform, and validate data for dashboards and ad hoc analyses.
- Maintain Data Quality: Collaborate with data engineering to ensure upstream data pipelines produce accurate, timely inputs for reporting.
- Drive Continuous Improvement: Identify opportunities to improve existing dashboards, automate manual reporting, and introduce new analytical capabilities — including AI-assisted tools where appropriate.
- Document and Standardize: Establish clear definitions, data dictionaries, and best practices for corporate and ARR-related metrics across the organization.
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