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# Analytics Engineer

- **Company:** Inc. Hq
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $140,000.0 - $175,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, BigQuery, Cloud Computing, Information Engineering, Data Systems, Data Warehousing, Cursor (Graphical User Interface Elements), Software Debugging, Google Analytics, Identity and Access Management, Job Scheduling, Python (Programming Language), Marketing Information Systems, Standard Sql, Next.js, Runbook, Shopify, Web Applications, Google Cloud, Google Data Studio, ReactJS, Large Language Models, Git, Data Layers, AI Platforms, Front End Software Development, Automation Anywhere, Api Management, Serverless Computing, Web Api
- **Published:** July 8, 2026
- **Apply:** https://chief-detective.clickup.com/forms/90151341936/f/2kyq0kvg-174375/MUY7QWZUE9R2J1Q6S3

## About the Role

1. 5+ years in analytics engineering or data engineering on a modern data stack
 2. Strong SQL (joins, window functions, CTEs) with hands-on, production experience in BigQuery on GCP
 3. Proven dbt ownership in production: modeling, testing, documentation, and job scheduling in dbt Cloud or an equivalent setup
 4. Python proficiency for automation services, custom API integrations, and light modeling
 5. Hands-on experience with marketing and e-commerce analytics data: ad platforms, Shopify, and GA4-style event data
 6. Strong debugging ability: you can trace a "this dashboard is wrong" issue back through reporting, models, pipelines, and source data
 7. Daily use of AI-assisted coding and agentic tools (Claude Code, Cursor, or comparable)
 8. Comfortable operating in a GCP environment (IAM, service accounts, secrets, serverless)
 9. A strong communicator who can work with non-technical and client stakeholders, translate business questions into durable data, and manage multiple priorities
 10. Solid Git, pull-request, and documentation habits (runbooks, metric definitions, system notes), 1. At least one production workflow built on LLM APIs or comparable AI services (Gemini, Vertex / Gemini Enterprise Agent Platform, or similar), beyond prompt experimentation
 2. Front-end or product engineering experience (React, Next.js) and interest in helping build our web apps and products
 3. Statistical modeling and predictive analytics (regression, time series, correlation) on e-commerce or marketing data
 4. 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)
 5. Relevant certifications such as Google Cloud Professional Data Engineer
 6. Appetite to mentor contractors or junior developers and grow into broader ownership of the analytics function as the team scales
 7. Equivalent practical experience in place of a formal degree is fully respected

## Description

We are hiring an AI-forward Senior Data & Analytics Engineer to own the data foundation everything else at Chief Detective is built on: the BigQuery and GCP data layer, the dbt models, and the reporting our team and clients depend on.

This role sits where analytics engineering meets product. You will build and own the foundation, and you will help turn it into the AI workflows and web apps we ship. The data you model here does not just feed dashboards, it powers the tools that our media-buying, creative, executive, and client teams rely on every day.

This is a hands-on, technically rigorous seat for someone who likes being in the weeds and making an impact end to end, from raw source data through the models and pipelines to the AI and products on top.

What You'll Do

 1. 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
 2. 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
 3. 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
 4. Model e-commerce data for forecasting and lightweight predictive work, including demand and revenue forecasting, regression, and correlation, where it drives real decisions
 5. Build and maintain the clean serving layer our AI workflows, agents, and internal web apps query, in place of direct API calls
 6. 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
 7. Help build our own web apps and products (React, Next.js) on the same GCP and BigQuery data layer
 8. 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


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