Senior Data & Analytics Manager
Role details
Job location
Tech stack
Job description
To achieve this, we're looking for a Data & Analytics Lead (f/m/d) to join our Revenue Operations team! In this role, you'll sit at the intersection of data science, BI analytics, and SaaS GTM strategy, owning our Tableau platform end-to-end, and driving our BI strategy. As such, you'll work alongside Data Engineers and BI Analysts but take point on analytics strategy, storytelling, and execution, ensuring our SaaS GTM teams always have clarity on performance, risks, and opportunities., * BI Ownership & Tableau Leadership: Own Tableau (Desktop, Prep, Server/Cloud): build, optimize, and govern dashboards; establish best practices; ensure performance and scalability
- Drive Tableau Adoption: Enable trust and reliance on reporting across GTM teams, from executives to front-line managers
- Analytics & Data Science: Own GTM analytics (funnel, pipeline, churn/retention, LTV/CAC) and apply techniques such as predictive modeling, cohort analysis, segmentation, and forecasting
- Insights & Storytelling: Run deep-dives into performance trends, identify leading vs. lagging indicators, and translate findings into clear business stories and recommendations for leadership
- Data Infrastructure & Governance: Partner with Data Engineers on pipelines/models across Salesforce, marketing automation, finance, and product usage; ensure data quality and governance with modern data stack
- Strategy & Leadership: Define BI strategy, align KPIs with GTM priorities, mentor analysts, and collaborate cross-functionally to anticipate needs and proactively deliver insights
Requirements
Do you have experience in Tableau?, * Analytics expertise: Multiple years of hands-on experience in BI / Data Science / GTM analytics.
- SaaS GTM Metrics Knowledge: Deep knowledge with SaaS go-to-market KPIs across the full customer lifecycle.
- Tableau Expertise: Proven ability to own dashboards end-to-end, including design, optimization, performance, and governance.
- SQL & Data Warehouse Skills: Advanced proficiency in SQL and experience with Snowflake or a comparable data warehouse environment.
- dbt & ETL Tools: Strong understanding of dbt and modern ETL/ELT tools, with experience building and maintaining data models and pipelines.
- Python/R: Ability to use Python or R for analysis, modeling, automation, or advanced statistical workflows.
- Applied Data Science Methods: Experience with predictive modeling, clustering, regression techniques, and time-series forecasting in practical business contexts.
- Communication & Storytelling: Excellent ability to translate complex data insights into clear, compelling stories for executives and non-technical stakeholders.
- Leadership Potential: Demonstrated capacity to lead from the front, drive cross-functional alignment, and inspire confidence in data-driven decisions.