Senior Data Analyst, Fivetran

• 4+ years of experience analyzing data
• Advanced proficiency in SQL and data modeling; fluency with large, complex datasets
• Experience using Looker, Tableau, or other BI tools
• Strong attention to detail and a track record of analytical rigor
• Excellent communication and storytelling — you know how to simplify the complex and influence decision-makers
• Comfortable taking ownership of ambiguous problems and designing end-to-end solutions
• Demonstrated success using analytics to drive decisions in a technical or product-focused environment
• Passion for building data products that drive business and operational impact

Desirable

• Fivetran is building data pipelines to power the modern data stack for thousands of companies
• We’re hiring a Senior Data Analyst to support our Engineering group and unlock insights from the rich internal data at Fivetran
• This role focuses on analyzing logs and engineering performance data to drive efficiency, reliability, and smarter decision-making
• You’ll lead high-impact, cross-functional projects and develop trusted relationships with engineering and product partners
• You’ll define and maintain core Engineering metrics, own key data pipelines, and help shape the team’s data culture and best practices
• If you love diving into technical systems, untangling messy datasets, and turning insight into action — this is your opportunity to make a real impact
• Define and own Engineering metrics for product and team performance; establish benchmarks and enable monitoring through dashboards and self-serve tools
• Analyze disparate datasets (e.g., logs, incidents, product telemetry, developer activity) to generate new insights and actionable recommendations
• Lead cross-functional projects with PMs, engineers, and analysts across domains like: Product reliability (uptime, latency, bug frequency), Engineering and Systems Efficiency, Operational processes (SLA compliance, Support team efficiency)
• Investigate anomalies and root causes, using deep dives and creative exploration across disparate datasets
• Own and enhance data infrastructure for Engineering Analytics — from metric definitions to pipelines to visualizations
• Shape data culture by mentoring junior analysts and establishing norms around best practices in metric design, analysis quality, and data storytelling