> Markdown version of [/jobs/ext/3468502-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/3468502-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer - **Company:** GoBrands, Inc. - **Location:** Philadelphia, PA, United States - **Salary:** $100,000.0 - $125,000.0 - **Contract:** Permanent contract - **Skills:** Microsoft Excel, Business Analytics Applications, Data Analysis, Microsoft Azure, Big Data, Information Systems, Databases, Data Integrity, Extract Transform Load (ETL), Data Security, Data Warehousing, Operational Databases, Query Optimization, Raw Data, SQL Databases, Expertise in Kinaxis, Snowflake, Information Technology, Looker Analytics - **Published:** September 9, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=02840fb707671c6c ## About the Role * Experience with ETL/ELT tooling (bonus points for dbt). * Strong knowledge of data warehousing concepts, big data technologies, and analytics platforms. Snowflake, Redshift, and/or Azure experience strongly preferred. * Exposure to supply chain, inventory, merchandise planning, or allocation data. You don't need years of planning experience, but familiarity with concepts like model stock, lead time, replenishment cycles, sell-through, and turn will get you productive faster. * Experience with marketing or CRM analytics: campaign performance, promotion lift, channel attribution, or customer segmentation. * Experience with demand planning or ERP systems such as JustEnough or Kinaxis. * Comfort working with AI-assisted development tools. We use assistants like Claude in our day-to-day analytics engineering work, and we value engineers who use them well: accelerating model development, SQL review, testing, and documentation while independently verifying outputs and knowing when the tool is the wrong answer. * Alcohol beverage or CPG category experience is a plus., * Bachelor's in Engineering, Computer Science, Information Systems, Business, or another quantitative discipline. * 3+ years of experience building data models that integrate complex and disparate data sources, using tools such as dbt. * Expert in SQL and database table design, able to write structured and efficient queries against large datasets. Advanced Excel skills for partner collaboration. * 1+ years building and maintaining reporting and dashboards in Looker or a comparable BI tool. * Strong analytical judgment: able to move from raw data to a clear recommendation, and to explain the 'so what' to business partners who will act on it. * Excellent communication skills, with the ability to translate business needs into tractable work items and to explain technical trade-offs to non-technical partners. * Top-notch organizational skills and the ability to manage multiple projects in a fast-paced environment. * Move fast, be a team player, always be learning, and give back. ## Description * Design, build, and maintain the production data models that power supply chain and merchandising decisions: sales and demand, inventory position and in-stock, damage and expiration, turn, vendor fill rate, purchase orders, and allocation. * Be the go-to data expert for these domains, with a deep understanding of our data warehouse and the processing layers feeding it. * Partner with Supply Chain divisional teams, Merchandising, Marketing, CRM, product managers, and engineers to translate business questions into durable models and self-serve tooling rather than one-off pulls. * Own data integrity, availability, transformation logic, and efficient data access for the domains you support. * Build and maintain reporting and dashboards in Looker that planners and merchants use daily, and retire the manual reporting they replace. * Support marketing and CRM analytics alongside the supply chain work: campaign and promotion performance, channel reporting. * Identify gaps in existing data, write data product specs, and work with engineering teams to get the right tracking in place. * Automate wherever possible, and build testing and monitoring so data quality problems surface before stakeholders find them. * Document your models so every stakeholder can find, understand, and trust the data without asking you first. ## Related Videos - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Making Data Warehouses fast. 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