Location: Remote (eligible US states: California, Colorado, Illinois, Massachusetts, New Jersey, New York, Ohio, Oregon, South Carolina, Texas, or Washington) Compensation: $140,000-175,000, commensurate with experience, plus bonus and benefits Chief Detective Chief Detective is a growth-focused agency and deep brand partner for D2C brands, especially in e-commerce. We build the analytics, pipelines, reporting, & web-apps our team and our clients actually make decisions on. Clean, well-modeled data here does not just feed dashboards, it empowers every employee and client to better achieve their goals. We move fast and care about measurable impact. About the Role 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 - 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.

• Design, develop, and maintain high-quality Power BI dashboards, KPI scorecards, and data models. • Translate business requirements into well-structured analytical solutions across manufacturing, supply chain, quality, finance, and commercial areas. • Ensure consistency and global alignment on metrics, definitions, and UI/UX standards. • Optimize Power BI performance, including DAX calculations, data modeling, and incremental refresh. • Partner with business process owners to enable self-service analytics. • Extract, model, and transform data from multiple global systems. • Support data pipeline development for analytics and reporting. • Ensure data quality and reliability across reporting ecosystems. • Help identify and scope early AI use cases. • Configure and support AI toolsets such as copilots and embedded SaaS AI. • Ensure safe and compliant AI usage in partnership with IT Security. • Provide training and documentation for AI-enabled tools.

• Own and improve canonical data models • Own and evolve the dbt models that transform raw source-system data into durable, reporting-ready tables. • Improve models over time by reducing duplication, clarifying grain, and documenting business logic. • Turn recurring reporting needs into reusable models rather than one-off queries. • Identify opportunities to simplify the data model and make reporting easier to understand and maintain. • Own the Lightdash semantic layer so core metrics are defined consistently and documented where they're used. • Maintain source-of-truth definitions, promoting and deprecating metrics as the business changes. • Improve the reporting ecosystem over time by making analytics more self-service and easier to use. • Build and maintain observability, testing, and monitoring across the analytics stack so data quality issues are identified early and resolved across source systems, transformations, and BI outputs. • Maintain strong development practices: version control, PR review, documentation, testing • Review analytics work for modeling quality and maintainability, and help analysts use the stack effectively. • Cross-train teammates so knowledge is shared rather than concentrated.

• support client enterprise ETL environment • lead and assist with the transition from Informatica to Oracle Data Integrator (ODI) • designing, developing, enhancing, testing, and deploying ETL solutions • migrating existing Informatica workflows to ODI • troubleshooting and maintaining ETL processes across Development, Test, and Production environments • administering the ETL platform while ensuring compliance with client cybersecurity, data governance, and operational standards