Data & Analytics Engineer

True Classic•Calabasas, CA

14h•Onsite

About The Position

True Classic is hiring a Data & Analytics Engineer to help own its data platform infrastructure and connect the data warehouse to AI, finance, and business stakeholder teams. The role supports core analytics engineering functions by building clean, well-structured, reliable data pipelines using established data engineering practices. It is suited to a hands-on, technically rigorous engineer who can contribute to a mid-build platform in a fast-paced, evolving environment. The role has global, omni-channel scope across product categories, countries, and sales and marketing channels, including DTC, retail, wholesale, marketplaces, and emerging channels. The engineer will extend and maintain the data platform, build business-facing data products, support AI applications, and collaborate across finance, AI, merchandising, operations, and analytics teams.

Requirements

At least 4 years of experience in data engineering or analytics engineering.
Strong understanding of data engineering best practices, including 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, including regression and time series.
Comfort working with non-technical stakeholders to translate business questions into data models and visualizations.
Proficiency with AI coding tools; daily use is expected.

Nice To Haves

NetSuite or ERP experience.
Experience with Daasity, Shopify/Amazon data, marketing attribution platforms (Meta CAPI, Google Ads, Triple Whale), Omni/Looker, or GitHub-based workflows.

Responsibilities

Build and maintain dbt models with attention to modularity, testing, documentation, and code quality.
Contribute to 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 using Daasity and BigQuery.
Help monitor and optimize cloud data infrastructure for cost and performance.
Deploy and maintain Omni dashboards on BigQuery for cross-functional stakeholders.
Structure financial data to support KPI tracking, forecasting, cost modeling, and channel-level P&L reporting.
Contribute to predictive models for demand forecasting, inventory planning, and revenue projections.
Build and maintain the serving layer for AI-team queries, using clean, modeled BigQuery tables instead of direct API calls.
Use AI coding tools daily to write dbt models, debug pipelines, and accelerate development.
Collaborate with the AI team to provide clean warehouse inputs for automation, machine-learning models, and real-time operations tools.
Identify opportunities for AI to automate data-quality checks, anomaly detection, and pipeline monitoring.
Work with finance to ensure financial data structures support forecasting and P&L reporting.
Partner with merchandising, operations, and analytics stakeholders to translate business questions into reliable data models and visualizations.