Responsibilities

• Act as the bridge between Data Engineering and Marketing Analytics, designing, building, and maintaining robust ETL pipelines and "Gold Layer" tables in Databricks.
• Develop and document semantic layers, data dictionaries, and markdown files designed specifically to train and enable AI agents for scalable, self-serve BI.
• Develop dashboards and analyses to surface business-critical KPIs and assess sales and marketing performance.
• Perform ad-hoc reporting and deep dives to uncover key insights across programs and initiatives to inform strategy and decision making.
• Partner with marketing stakeholders to translate complex business requirements into scalable data models and analytics-ready datasets.
• Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices.

Requirements

• Bachelor's in Statistics, Computer Science, Business, Economics or other related analytical fields.
• 5+ years of relevant analytics, business intelligence, artificial intelligence or data engineering work experience.
• Expert in SQL, python, DBT and visualization platforms (Tableau preferred).
• Hands-on experience working with Databricks (or similar lakehouse architectures) to transform and model large datasets.
• Experience building data backends, semantic layers, and structured documentation optimized for AI ingestion and agentic workflows.
• Strong analytical and critical thinking skills with experience in data analysis.
• Comfortable leveraging AI tools to accelerate data transformation, analysis, and visualization workflows.
• Experience supporting Sales, Product or Marketing teams.

Preferred

• Experience building interactive applications using Streamlit or Claude workflows.
• Experience building web applications
• Experience working in web analytics
• Sales and marketing experience including familiarity with MarTech tools.
• Navigate large, multifaceted datasets to answer hard questions — drawing on the right analytical or statistical approach for each problem.
• Data Science, machine learning or predictive analytics experience.
• Actively builds AI fluency relevant to BI and analytics work, including prompt engineering for data tasks, exploring AI-assisted features in tools, and staying current on how AI is reshaping the analytics landscape.
• Comfort working in an exploratory environment, testing and iterating on new data sources and methodologies.