Senior Data Analytics Engineer

Envoy • San Francisco, CA

3d • Onsite

About The Position

Envoy protects the places the world relies on most by unifying people, spaces, and communications in one secure, integrated workplace management platform and ecosystem. More than 16,000 workplaces around the world trust Envoy to run secure, compliant, and connected operations across every location. From manufacturing sites and data centers to life sciences labs, healthcare facilities, and corporate headquarters, Envoy unifies visitor management, risk assessment, mailroom management, digital signage software, resource booking, and emergency management into one integrated platform. With deep integrations across access control, identity, compliance screening, and collaboration tools—including LenelS2, Brivo, Genetec, Honeywell, Cisco Meraki, Okta, Microsoft Azure, Microsoft Teams, Slack, ServiceNow, DocuSign, Avigilon Alta, and Descartes Visual Compliance—Envoy helps organizations reduce risk, stay audit-ready, and operate with clarity at scale. Learn more at envoy.com About the role We are a top-notch data organization with a high bar for our code, systems, practices, and people. At Envoy, we don’t just "report" on data; we build the engine of the company. As a Senior Analytics Engineer, you are a "Full-stack" Data IC. You will treat our data assets as products—shipping reliable, high-quality data software. You will help build our Intelligence Platform, leveraging the power of our Semantic Layer, RAG, and MCP, to enable agentic self-service insights and autonomous intelligence. You'll also be a key partner to Product Teams, helping guide prioritization and feature development efforts by turning data into a strategic advantage. We have a modern data stack that includes Redshift, Databricks, dbt, Airflow, Omni, Segment, and Estuary. This is an on-site position that requires 4 days a week (Monday - Thursday) in our San Francisco HQ.

Requirements

• 5+ years of experience in Analytics Engineering, Data Science, or Data Engineering, with a strong track record in high-growth startups.
• Mastery of dbt & SQL: You write "software-grade" SQL and have managed complex, large-scale dbt projects.
• Python & AI Literacy: Proficient in Python for building ML workflows and interacting with LLM APIs and Vector Databases.
• Engineering Discipline: Hands-on experience with modern engineering workflows, including Git, CI/CD, and automated data testing.
• An AI-First Builder: You are a practitioner of "vibe-coding"—leveraging LLMs to accelerate development and automate boilerplate—while maintaining the "Human-at-the-Lead" judgment to ensure system integrity.
• A Product Thinker: You treat data as a product. You understand that your work is only as good as the decisions it enables for PMs and the efficiency it provides for AI agents.
• A Lifelong Learner: You live to learn new things and stay up to date on new technologies and techniques. You are inspired by what people know inside and outside the company and are eager to incorporate that knowledge into your work.
• Proactive & Autonomous: You don’t wait for a ticket to fix a bottleneck or a flawed data model. You look for problems and inefficiencies and find elegant solutions before they become major issues.
• Emotionally Mature & Humble: You care about being effective over being right. You thrive in a high-standards environment where feedback is a gift.

Nice To Haves

• Product Analytics Partnership: Proven experience working with Product teams to influence roadmap priorities and measure the success of feature releases.
• Agentic Workflows: Experience developing with or managing agentic workflows—understanding both the power and the limitations of autonomous loops.
• Modern Tooling & Streaming: Experience with MCP servers, real-time data movement via Estuary, or Streaming Analytics (e.g. Kafka, Flink).
• Vector DB Experience: Familiarity with OpenSearch or other vector stores for retrieval-augmented generation (RAG).

Responsibilities

• Develop the Intelligence Platform: Help build and scale our semantic layer and RAG infrastructure, ensuring our data is modeled for both human consumption and autonomous agentic workflows.
• Guide Product Strategy: Partner closely with Product teams to drive prioritization and feature development through deep-dive analysis, KPI definition, and robust data modeling.
• Support ML & Data Science Workflows: Build and maintain the data pipelines and feature sets required for machine learning models and advanced data science projects.
• Champion Engineering Rigor: Lead the team in applying software engineering best practices, including version control, CI/CD pipelines, and rigorous peer reviews.
• Mentor and Multiply: Act as a technical resource for the team, providing guidance on solution design and raising the bar for code quality and architectural intuition.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 101-250 employees