Senior Analytics Engineer

What You'll Do
Own our dbt models and the BigQuery data layer end to end: design, test, document, schedule, and ship clean, reliable data from staging through marts following best practices
Build and maintain the pipelines that bring marketing, e-commerce, and fulfillment data into our warehouse, and stand up custom extractions when off-the-shelf connectors fall short
Be the bridge between data and the business: turn questions from across the company into trusted KPI definitions, Looker Studio reporting, and reusable data models
Model e-commerce data for forecasting and lightweight predictive work, including demand and revenue forecasting, regression, and correlation, where it drives real decisions
Build and maintain the clean serving layer our AI workflows, agents, and internal web apps query, in place of direct API calls
Use AI daily: write dbt models and debug pipelines with Claude Code and Cursor, and build practical AI workflows on Google's AI stack (Gemini and the Gemini Enterprise Agent Platform, formerly Vertex AI) for enrichment, QA, classification, and automation
Help build our own web apps and products (React, Next.js) on the same GCP and BigQuery data layer
Keep our GCP environment running safely (IAM, service accounts, secrets, serverless) and keep a pulse on new GCP and AI capabilities so we stay ahead of the game
What We're Looking For
5+ years in analytics engineering or data engineering on a modern data stack
Strong SQL (joins, window functions, CTEs) with hands-on, production experience in BigQuery on GCP
Proven dbt ownership in production: modeling, testing, documentation, and job scheduling in dbt Cloud or an equivalent setup
Python proficiency for automation services, custom API integrations, and light modeling
Hands-on experience with marketing and e-commerce analytics data: ad platforms, Shopify, and GA4-style event data
Strong debugging ability: you can trace a "this dashboard is wrong" issue back through reporting, models, pipelines, and source data
Daily use of AI-assisted coding and agentic tools (Claude Code, Cursor, or comparable)
Comfortable operating in a GCP environment (IAM, service accounts, secrets, serverless)
A strong communicator who can work with non-technical and client stakeholders, translate business questions into durable data, and manage multiple priorities
Solid Git, pull-request, and documentation habits (runbooks, metric definitions, system notes)
Nice to Have
At least one production workflow built on LLM APIs or comparable AI services (Gemini, Vertex / Gemini Enterprise Agent Platform, or similar), beyond prompt experimentation
Front-end or product engineering experience (React, Next.js) and interest in helping build our web apps and products
Statistical modeling and predictive analytics (regression, time series, correlation) on e-commerce or marketing data
Looker Studio tuning with cost and performance awareness, deeper GCP ops (Cloud Run / Cloud Functions, Cloud Scheduler / Workflows, Pub/Sub), or data observability patterns (freshness SLAs, alerting, anomaly detection)
Relevant certifications such as Google Cloud Professional Data Engineer
Appetite to mentor contractors or junior developers and grow into broader ownership of the analytics function as the team scales
Equivalent practical experience in place of a formal degree is fully respected
Benefits
Competitive salary: $140,000-175,000, commensurate with experience
Comprehensive benefits: group medical, dental, and vision coverage; short- and long-term disability; life insurance; 401(k) eligibility after one year of service with matching contributions; paid time off; sick leave; an Employee Assistance Program, and more
Professional growth: the opportunity to shape our analytics future, build durable systems, and grow quickly with a sharp, entrepreneurial team
Innovative culture: work closely with leadership and a team focused on building practical, high-impact analytics systems
If you are excited to own a modern analytics foundation, improve data reliability, and build useful automation and AI on top of it, we would love to hear from you.

Skills Required
5+ years in analytics engineering or data engineering on a modern data stack
Strong SQL (joins, window functions, CTEs) with production BigQuery experience on GCP
Proven dbt ownership in production: modeling, testing, documentation, and job scheduling (dbt Cloud or equivalent)
Python proficiency for automation services, custom API integrations, and light modeling
Hands-on experience with marketing and e-commerce analytics data: ad platforms, Shopify, GA4-style event data
Strong debugging ability tracing issues from dashboards back through reporting, models, pipelines, and source data
Daily use of AI-assisted coding and agentic tools (Claude Code, Cursor, or comparable)
Comfortable operating in a GCP environment (IAM, service accounts, secrets, serverless)
Strong communicator able to translate business questions into durable data and manage multiple priorities
Solid Git, pull-request, and documentation habits (runbooks, metric definitions, system notes)
Production experience building and owning pipelines that ingest marketing, e-comm, and fulfillment data