Own end-to-end development of advanced analytics, dashboards, and data products focused on customer success metrics such as renewals, adoption, churn risk, NDR, and value realization.
Translate complex datasets into clear, compelling narratives and actionable recommendations for senior leaders and partners across multiple functions.
Design, build, and maintain scalable data pipelines integrating large datasets across subscription, services, usage, and customer health domains.
Ensure data quality, consistency, and governance to support reliable, enterprise-grade analytics and reporting.
Develop and deploy predictive and prescriptive models to surface churn risk, expansion signals, and consumption health.
Build and operationalize ML- and LLM-powered insight workflows and agents that automate analysis and enable next-best-action recommendations.
Create executive-ready dashboards and reports using Power BI (and related tools), optimized for clarity, performance, and decision-making.
Embed advanced metrics, forecasting, and insights generated by machine learning directly into analytics experiences.
Partner closely with Customer Success, Engineering, Professional Services, Sales Operations, and Product teams to align analytics solutions with business priorities.
Act as a trusted analytics advisor, influencing decisions through data rather than formal authority.
Continuously improve analytics processes, performance, and scalability.
Contribute to standards, procedures, and reusable frameworks across the analytics ecosystem.

Requirements

Bachelor’s or graduate degree in Computer Science, Data Science, Business, or a related field.
3+ years of hands-on experience in advanced analytics, data science, business intelligence, or data engineering roles.
Strong expertise in SQL, Python, Spark, and cloud platforms (Azure, AWS, or GCP).
Demonstrable experience in building scalable data pipelines, advanced dashboards, and executive-level reporting (Power BI, Tableau, Adobe Workspace).
Demonstrated track record of developing and deploying ML/AI models in production environments.
Solid understanding of SaaS business models and Customer Success domains (renewals, adoption, churn, value realization).
Superb communication skills with the ability to clearly explain complex analysis to senior and non-technical audiences.
Strong organizational skills with the ability to manage multiple initiatives and deliver outcomes in fast-paced environments.

Benefits

Health insurance
401(k) matching
Paid time off
Professional development opportunities

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

SQLPythonSparkdata engineeringadvanced analyticsbusiness intelligenceML modelsdata pipelinespredictive modelsprescriptive models

Soft skills

communication skillsorganizational skillsinfluencing decisionsnarrative buildingactionable recommendationsprocess improvementscalability management