Senior AI Data & Analytics Engineer II

Samsara is hiring a Senior AI Data & Analytics Engineer II to build the data foundation for AI and self-serve analytics across its Marketing Data and Analytics organization. The role owns marketing data infrastructure, semantic models, and AI-enabled data products, while shaping the technical roadmap and leading cross-functional initiatives.

Responsibilities:

• Partner on defining the strategy and multi-quarter roadmap for MDA's data and AI foundation
• Architect and own Samsara's marketing data infrastructure: ETL/ELT pipelines, Databricks Gold Layer tables, and the core data models that power segmentation, targeting, automation, and analytics
• Design the semantic layers, data dictionaries, and structured documentation that enable self-serve BI and conversational analytics, partnering with the BI team to ensure core sales and marketing KPIs are built on trusted, well-modeled data
• Translate ambiguous business questions from Marketing, Sales, and R&D into clear requirements and scalable solutions, working autonomously without dedicated PM support and influencing decisions with data and sound reasoning
• Lead complex, cross-team initiatives end to end: stand up new data pipelines and integrations quickly, manage risk across interdependent teams, and own the reliability and data quality of what ships
• Serve as the senior technical lead for the team's hardest problems: raise the bar for engineering quality and code review, remove bottlenecks that slow multiple teams, and mentor engineers and analysts
• Drive the strategic use of AI to solve ambiguous, high-impact problems: build AI-enabled data products and agents, with the integrations, retrieval pipelines, and evaluation practices that keep them reliable and trustworthy
• Identify and automate manual workflows with AI across Marketing and GTM teams, taking ideas from concept to prototype to production and influencing org roadmaps
• Ship high-quality Python and SQL, increasingly by directing agentic coding tools, while holding a high bar for reviewing and verifying AI-generated work before it reaches production
• 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:

• 8+ years of experience in data engineering, analytics engineering, or AI engineering, including deep hands-on data engineering experience
• Expert-level Python and SQL, with strong hands-on data modeling and DBT experience
• Deep experience with data warehouse and lakehouse architectures, ETL/ELT, and the modern data stack, including hands-on Databricks (or similar, such as Snowflake or BigQuery)
• Experience building semantic layers and structured documentation optimized for AI ingestion and agentic workflows
• Agentic coding tools (e.g., Claude Code, Cursor) are part of your regular workflow, and you verify and take ownership of AI-generated output
• Experience building, shipping, evaluating, and monitoring LLM or agent systems in production
• Track record of leading complex, cross-team data initiatives, including independent requirements gathering with technical and non-technical stakeholders
• Experience supporting Marketing, Sales, or Product teams
• This role is open to candidates residing in the US
• Experience working with Databricks and building AI Agents within the ecosystem
• Software engineering background beyond data or AI engineering, with a track record of turning ambiguous, unfamiliar problems into well-architected, delivered solutions
• Expertise in data visualization and building dashboards for sales and marketing KPIs
• Familiarity with go-to-market data and systems: CRM (e.g., Salesforce), marketing automation, web analytics, and MarTech tools
• Experience building interactive applications with Streamlit, Claude workflows, or web frameworks
• Data science, machine learning, or predictive analytics experience
• Comfort working in an exploratory environment, testing and iterating on new data sources and methodologies