GCP Data Engineer - Retail AI & Pipelines

MathCo; Atlanta (GA); Hybrid; USD 95,000 - 150,000; Full time

Job summary

MathCo in Atlanta is hiring Data Engineers to design scalable data pipelines and semantic retrieval systems for major B2C retail clients. You will work with GCP services such as BigQuery, Vertex AI, Cloud Storage, and Dataflow to enable enterprise-grade analytics and AI applications.

This hybrid role focuses on building ETL/ELT pipelines, vector search, and feature stores, with emphasis on data quality, governance, and production-scale architectures.

Qualifications

• 4–7 years in data engineering, analytics engineering, or cloud engineering.
• Strong proficiency in SQL, Python, and cloud-native data pipelines.
• Hands-on experience with GCP, especially BigQuery, Cloud Storage, and Dataflow.
• Experience with feature engineering, predictive modeling support, or LLM/GenAI data preparation.
• Experience in Retail, Fashion Retail, or B2C Commerce strongly preferred.
• Bachelor's degree in Computer Science, Engineering, Data Science, or related field.

Responsibilities

• Build scalable, production-grade ETL/ELT pipelines using Python, SQL, and GCP-native services.
• Develop semantic retrieval, vector search, and feature engineering workflows supporting enterprise AI systems.
• Design and optimize BigQuery data models, partitioning strategies, and performance tuning.
• Implement data quality, validation, and monitoring frameworks for high-reliability retail data environments.

Skills

SQL

Python

GCP proficiency

Data pipelines design

Retail domain knowledge

Education

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

Tools

BigQuery

Cloud Storage

Dataflow

Pub/Sub

GitHub Actions

Job description

MathCo in Atlanta is hiring Data Engineers to design scalable data pipelines and semantic retrieval systems for major B2C retail clients. You will work with GCP services such as BigQuery, Vertex AI, Cloud Storage, and Dataflow to enable enterprise-grade analytics and AI applications.

This hybrid role focuses on building ETL/ELT pipelines, vector search, and feature stores, with emphasis on data quality, governance, and production-scale architectures.