Senior Analytics Engineer

Axon•Seattle, WA

8h•Onsite

About The Position

Join Axon and be a Force for Good. At Axon, we’re on a mission to Protect Life. We’re explorers, pursuing society’s most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products, we work better together. We connect with candor and care, seeking out diverse perspectives from our customers, communities and each other. Life at Axon is fast-paced, challenging and meaningful. Here, you’ll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter. Your Impact This is a Senior Analytics Engineering role focused on Product Analytics. You will define and build the analytics architecture that helps us understand how our products are used across Axon’s ecosystem. This includes designing scalable data models, event schemas, and semantic layers that turn complex, event-driven data into trusted, business-ready datasets. You will own the analytical layer end-to-end — from instrumentation standards and modeling to metric definitions and governance — ensuring product data is reliable, intuitive, and easy to use. Your work will enable data scientists, analysts, product managers, and engineers to understand product adoption, shape strategy, and improve user experiences. A core part of the role is leading our data visualization strategy: designing and maintaining scalable dashboards and reporting frameworks that support self-serve analytics without sacrificing metric consistency or trust. You will define visualization standards and best practices so insights are clear, aligned, and decision-oriented. At this stage of the company, the challenge is building analytics systems that are both scalable and simple. You will establish common interfaces, shared event schemas, and practical guardrails that make it straightforward for engineering teams to instrument products correctly and for stakeholders to quickly get value from the data. This means striking the right balance between standardization and flexibility as we grow. Success in this role requires strong architectural judgment. You’ll make early decisions in data modeling, metric design, and instrumentation that prevent fragmentation and analytical debt later. You must be comfortable balancing speed with long-term scalability in a fast-growing environment, and confident partnering directly with software and hardware engineers to influence instrumentation choices that support durable, business-aligned analytics. Location: Seattle, Axon office, Tuesday through Friday Reports to: Principal Product Manager, Product Analytics

Requirements

Bachelor’s degree in Computer Science, Engineering, Analytics, or a related technical field (or equivalent practical experience).
6+ years of experience in analytics engineering, data engineering, or a related role with strong exposure to product analytics.
Advanced SQL skills and deep experience building and maintaining production-grade data models in a modern cloud data warehouse (e.g., Snowflake, BigQuery, Redshift).
Proven experience using transformation frameworks such as dbt (or similar) to build tested, version-controlled analytics layers.
Hands-on experience designing and building dashboards in modern BI tools (e.g., Looker, Tableau, Sigma, Power BI, etc.).
Experience defining metric frameworks, shared definitions, and semantic layers that enable consistent self-serve analytics.
Strong understanding of event-driven data, product telemetry, and instrumentation best practices.
Experience implementing data quality checks, testing frameworks, and documentation standards to support a trusted “single source of truth.”
Strong communication skills with the ability to partner effectively with product managers, engineers, analysts, and leadership.
A collaborative, team-first mindset with a desire to raise the bar for analytics quality and clarity.

Nice To Haves

Experience partnering closely with engineering teams on event schema design and instrumentation standards.
Experience supporting product experimentation frameworks (A/B testing, feature flags, adoption metrics).
Working knowledge of Python for analytical workflows or data validation.
Familiarity with orchestration tools (Airflow, Dagster, etc.) and ingestion tooling (Fivetran, etc.).
Experience operating in fast-paced, high-growth environments with a high degree of ownership.
Passion for public safety, social impact, or building products that make a difference.

Responsibilities

Analytics Architecture & Foundations Set the direction for our analytics layer, including modeling patterns, semantic layer standards, and shared metric definitions. Ensure product telemetry flows into clean, well-structured datasets that teams can trust and use confidently. Establish scalable conventions that reduce ambiguity and prevent metric drift as the organization grows.
Event Strategy & Instrumentation Partner closely with product managers and engineering teams to define and maintain a scalable event tracking framework and “source of truth” event schema. Establish clear taxonomies, naming conventions, user properties, and key product events required to measure adoption and product health. Promote “right the first time” instrumentation by working directly with frontend and backend engineers to reduce downstream rework.
Data Modeling & Visualization Build and maintain well-tested data models that transform raw telemetry into intuitive, business-aligned datasets. Design, build, and iterate on high-impact dashboards and reporting frameworks that power product decision-making, experimentation analysis, and executive visibility. Define and evolve visualization standards to enable self-serve analytics while preserving clarity, consistency, and trust in the numbers.
Operational Rigor & Governance Establish data quality standards, testing practices, documentation norms, and service expectations for Product Analytics. Define how we measure the health and effectiveness of our analytics systems. Reduce reliance on tribal knowledge by creating clear, discoverable documentation and scalable processes that support future growth and compliance needs.