Senior Analytics Engineer

Remote (Offshore welcome) | Full-Time

About Mood

Mood is not your average e-commerce company - we're building the future of legal cannabis in the U.S. through a digital-first, customer-obsessed approach. We're growing fast, powered by a team that's just as passionate about building a seamless, personalized customer experience as they are about transforming the cannabis industry.

From our rapidly expanding product catalog to our innovative use of analytics, machine learning, and AI, data is at the core of every decision we make. As we scale, we're evolving our data platform into an AI-enabled analytics stack that powers experimentation, automation, and real-time decision-making across the business.

The Role

We're looking for a Senior Analytics Engineer to own the architecture and reliability of Mood's analytics layer. Reporting to the VP of Data & Analytics, this role sits at the intersection of data engineering, analytics, and business intelligence.

You'll design and maintain the data models, transformation pipelines, and semantic layers that power our dashboards, experimentation frameworks, and AI-driven insights. Your work will ensure that the business operates from a single source of truth while enabling faster insights, better automation, and scalable analytics infrastructure.

This role is ideal for someone who enjoys building clean, scalable analytics systems and is excited about enabling an AI-ready data environment.

What You'll Do

Analytics Architecture & Data Modeling

Design and maintain scalable data models that support reporting, experimentation, and machine learning.

Own the semantic layer and ensure consistent metric definitions across dashboards, analyses, and data products.

Develop and maintain transformation pipelines that turn raw data into clean, trusted analytics datasets.

Partner with analytics and business teams to translate analytical needs into well-structured data models.

Ensure the analytics layer is designed to support AI, experimentation frameworks, and predictive modeling.

Data Pipeline Development & Reliability

Build and maintain reliable data pipelines using modern transformation frameworks.

Improve data freshness, performance, and scalability across the analytics stack.

Implement testing, monitoring, and validation frameworks to ensure data quality and reliability.

Work closely with data engineering to optimize warehouse performance and pipeline efficiency.

Support ingestion and modeling of new data sources across product, marketing, CX, and operations.

Analytics Enablement & Reporting Infrastructure

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Power the dashboards and reporting used across marketing, product, operations, and leadership.

Optimize the BI layer to improve performance, usability, and trust in analytics outputs.

Reduce manual reporting by building reusable datasets and scalable reporting infrastructure.

Partner with analysts to ensure analytics workflows are efficient and well-supported by the data layer.

AI & Advanced Analytics Enablement

Design data models that support experimentation frameworks, predictive models, and AI applications.

Collaborate with data science to operationalize model outputs into reporting and decision workflows.

Ensure clean, well-documented datasets that enable faster development of machine learning and AI-driven products.

Help evolve Mood's data platform toward a modern AI-enabled analytics architecture.

Documentation & Data Governance

Maintain clear documentation for data models, metrics, and transformation logic.

Define and enforce standards for data quality, metric definitions, and modeling best practices.

Ensure stakeholders can easily understand and trust the data powering business decisions.

Contribute to a culture of data ownership, transparency, and high-quality analytics.

What We're Looking For

Experience & Skills

• 4–7+ years of experience in analytics engineering, data engineering, or business intelligence.
• Strong SQL skills and experience building scalable transformation pipelines.
• Hands-on experience with modern analytics engineering tools (dbt or similar highly preferred).
• Experience working with cloud data warehouses (BigQuery strongly preferred).
• Experience designing data models that power BI tools such as Looker, Looker Studio, Tableau, or similar.
• Strong understanding of dimensional modeling, semantic layers, and analytics data architecture.

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• Experience building reliable data pipelines and implementing data testing/validation frameworks.
• Comfort working closely with analysts, engineers, and business stakeholders.
• Experience supporting experimentation, growth analytics, or product analytics is a strong plus.
• Familiarity with data structures required for machine learning or AI-driven analytics is a plus.
• Strong documentation habits and a commitment to building trusted analytics infrastructure.
  
  

Working Style

• Systems thinker who enjoys designing scalable analytics infrastructure.
• Strong collaborator who can bridge analytics, engineering, and business needs.
• Detail-oriented and committed to data quality and reliability.
• Comfortable operating in fast-moving startup environments.
• Curious about emerging AI and data tooling and excited to help build toward an AI-enabled analytics stack.
  
  

Why Join Mood?

Build the foundation of our data platform. You'll shape how analytics works across the company.

Own real architecture. This role goes beyond dashboards — you'll design the systems that power insights.

Help enable AI. Your work will directly support machine learning, experimentation, and intelligent automation.

Work with a modern data stack. Collaborate with analytics, data science, growth, and leadership teams who value speed and accuracy.

High-impact environment. Data directly drives decisions across marketing, product, and operations.

Remote-first. Work from anywhere with a sharp, ambitious, collaborative team.