Data & Analytics Engineer

Requirements

• 4+ years of experience in data engineering or analytics engineering
• Strong understanding of data engineering best practices: pipeline design, data modeling, testing, and documentation
• Hands-on experience with dbt Cloud, Google BigQuery, and SaaS API pipeline development
• Strong SQL skills including joins, window functions, and CTEs
• Python proficiency for pipeline scripting, API integrations, and light modeling
• Familiarity with statistical modeling and predictive analytics (regression, time series)
• Comfortable working with non-technical stakeholders to translate business questions into data models and visualizations
• Proficiency with AI coding tools - daily use expected
• NetSuite or ERP experience
• Daasity, Shopify/Amazon data
• Marketing attribution platforms (Meta CAPI, Google Ads, Triple Whale),
• Omni/Looker, GitHub-based workflows
• Workplace Arrangement
• This role is on-site (5x week in office) based in Calabasas, CA.
• Compensation and Benefits

Benefits

Additional Information

True Classic is hiring a Data & Analytics Engineer to partner in owning our data platform infrastructure and to serve as a key builder connecting our data warehouse to our AI, finance, and business stakeholder teams. This role will support core analytics engineering functions, ensuring clean, well-structured, and reliable data pipelines built to best practice standards. This role is ideal for someone who is hands-on and technically rigorous, with a strong command of data engineering best practices - including pipeline design, data modeling, testing, and documentation - and can contribute meaningfully to a mid-build platform in a fast-paced, evolving environment. All of True Classic's roles are global and omni-channel, leading designated areas of accountability across all product categories, countries, and sales and marketing channels. This role will have impact across DTC, retail, wholesale, marketplaces, and emerging channels, ensuring strategic alignment and executional rigor across the enterprise. Areas of Accountability Extend & Maintain the Data Platform Build and maintain dbt models following best practices for modularity, testing, documentation, and code quality Contribute to completion of open data model workstreams across inventory, media, and product functions Expand data source connectivity and pipeline coverage across marketing and fulfillment systems Maintain and improve ETL/ELT workflows via Daasity and BigQuery Help monitor and optimize cloud data infrastructure for cost and performance Bridge Data to the Business Deploy and maintain Omni dashboards on top of BigQuery for cross-functional stakeholders Support business KPI tracking by structuring financial data for forecasting, cost modeling, and channel-level P&L Contribute to predictive models for demand forecasting, inventory planning, and revenue projections Build and maintain the serving layer that the AI team queries - clean, modeled BigQuery tables in place of direct API calls AI-Augmented Development Use AI coding tools (Claude Code, Cursor, Copilot) daily to write dbt models, debug pipelines, and accelerate development Collaborate with the AI team to ensure the warehouse serves their applications with clean inputs for automation, ML models, and real-time ops tools Identify opportunities where AI can automate data quality checks, anomaly detection, and pipeline monitoring Cross Functional Collaboration Work with finance to ensure financial data structures support forecasting and P&L reporting needs Partner with the AI team to ensure warehouse outputs support downstream automation and machine learning applications Work alongside merchandising, operations, and analytics stakeholders to translate business questions into reliable data models and visualizations

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