Growth Marketing Data Engineer: Pipelines & Analytics

Living Spaces Furniture; La Mirada (CA); Sur place; USD 90 000 - 120 000; Plein temps

Avantages offerts par ce poste

Medical insurance

Dental insurance

Vision insurance

401(k) with company match

Vacation

Paid sick leave

Health Spending Account

Employee Assistance Program

Holiday pay

Life insurance

Résumé du poste

Living Spaces Furniture is seeking a Marketing Data Engineer to design, build, and automate data infrastructure powering Marketing Analytics Intelligence. You will optimize pipelines, ingest data from multiple sources, and model datasets to support reporting and decision-making.

collaborations with analysts and stakeholders to ensure accurate, scalable data; experience with GCP, SQL, Python, and ETL/ELT tools is essential for driving reliable analytics across campaigns and ROAS metrics.

Qualifications

• Bachelor's degree in CS/DS/IS or related field; 3+ years in data engineering or ETL/ELT.
• Hands-on automation of data ingestion, transformation, and integration.
• Experience with digital marketing data APIs and campaigns desired.

Responsabilités

• Design, build, and maintain automated data pipelines and workflows.
• Develop, document, and optimize data models and datasets for analytics.
• Manage end-to-end lifecycle of data pipelines from design to deployment.
• Monitor pipelines, fix data issues, and ensure timely reporting.
• Collaborate with analysts to translate requirements into scalable solutions.
• Apply AI/LLM tools to enhance data workflows and documentation.

Connaissances

SQL

Python

Data pipelines

Formation

Bachelor's degree in Computer Science, Data Engineering, Data Science, Information Systems, or related field

Outils

Google Cloud Platform

dbt

Airflow

Alteryx

Git

Tableau/Power BI

Description du poste

Living Spaces Furniture is seeking a Marketing Data Engineer to design, build, and automate data infrastructure powering Marketing Analytics Intelligence. You will optimize pipelines, ingest data from multiple sources, and model datasets to support reporting and decision-making.

collaborations with analysts and stakeholders to ensure accurate, scalable data; experience with GCP, SQL, Python, and ETL/ELT tools is essential for driving reliable analytics across campaigns and ROAS metrics.