Data & Analytics Engineer

True Classic

Calabasas (CA)

On-site

USD 130,000 - 190,000

Full time

14 days+

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Benefits offered by this job

True Classic merchandise allowance

401(k) plan with 3% company match

Job summary

True Classic is seeking a Data & Analytics Engineer to own and optimize our data platform infrastructure. You will build clean, reliable data pipelines and serve as a bridge between data and AI, finance, and business teams.

This hands-on role emphasizes pipeline design, data modeling, testing, and documentation within a fast-paced environment, with on-site work in Calabasas, CA.

Qualifications

4+ years of experience in data engineering or analytics engineering.
Hands-on with dbt Cloud, BigQuery, and SaaS API pipelines.
Strong SQL with joins, window functions, and CTEs.
Python for pipeline scripting and integrations.
Ability to translate business questions into data models and visualizations.
Familiar with predictive analytics and time series.

Responsibilities

Extend and maintain the data platform with modular, tested pipelines.
Bridge data to the business by enabling KPI tracking and forecasting.
Contribute to AI-augmented development and model-ready data serving layers.
Collaborate cross-functionally with finance, AI and analytics stakeholders.

Skills

SQL

Python

Data modeling

Stakeholder comms

Tools

dbt Cloud

Google BigQuery

SaaS API pipelines

Job description

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 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

Qualifications

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

Preferred Qualifications