Senior AI Data & Analytics Engineer II

Samsara•Dallas, TX

About The Position

As part of Samsara's Marketing Data and Analytics (MDA) Engineering team, this role shapes the technical roadmap alongside BI and Data Science peers. The engineer architects core Databricks pipelines, Gold Layer tables, and semantic models, turning ambiguous business challenges into data products, autonomous agents, and cross-functional automation. The role is open to candidates residing in the US. The position combines senior technical leadership in data engineering with strategic development of AI-enabled products and workflows. It owns the reliability and quality of marketing data infrastructure and helps enable trusted, self-service analytics and conversational analytics across Marketing, Sales, and related teams.

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 a similar platform such as Snowflake or BigQuery.
Experience building semantic layers and structured documentation optimized for AI ingestion and agentic workflows.
Regular use of agentic coding tools such as Claude Code or Cursor, with responsibility for verifying and owning AI-generated output.
Experience building, shipping, evaluating, and monitoring LLM or agent systems in production.
Track record leading complex, cross-team data initiatives, including independently gathering requirements from technical and non-technical stakeholders.
Experience supporting Marketing, Sales, or Product teams.

Nice To Haves

Experience working with Databricks and building AI agents within its 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, including CRM such as 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.

Responsibilities

Partner on defining the strategy and multi-quarter roadmap for MDA's data and AI foundation.
Architect and own marketing data infrastructure, including ETL/ELT pipelines, Databricks Gold Layer tables, and core data models for segmentation, targeting, automation, and analytics.
Design semantic layers, data dictionaries, and structured documentation to enable self-serve BI and conversational analytics; partner with BI to ensure sales and marketing KPIs use trusted, well-modeled data.
Translate ambiguous business questions from Marketing, Sales, and R&D into clear requirements and scalable solutions, working autonomously and influencing decisions with data and sound reasoning.
Lead complex, cross-team initiatives end to end, including quickly standing up pipelines and integrations, managing risk across interdependent teams, and owning shipped data reliability and quality.
Serve as the senior technical lead for the team's hardest problems, raise engineering and code-review quality, remove bottlenecks, and mentor engineers and analysts.
Drive strategic use of AI to address ambiguous, high-impact problems by building AI-enabled data products and agents, including integrations, retrieval pipelines, and evaluation practices that support reliability and trustworthiness.
Identify and automate manual workflows with AI across Marketing and GTM teams, taking ideas from concept through prototype to production and influencing organizational roadmaps.
Ship high-quality Python and SQL, increasingly by directing agentic coding tools, and review and verify AI-generated work before production.
Champion and model Samsara's cultural principles: Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, and Win as a Team.