Marketing Data Engineering Lead

Advanced Micro Devices, Inc.; Santa Clara (CA); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

AMD benefits at a glance

Job summary

Advanced Micro Devices, Inc. is seeking a hands-on Manager of Marketing Data Engineering within the Marketing Analytics team.

You will build scalable data pipelines, canonical datasets, and trusted data assets enabling advanced analytics, ML, and campaign optimization. The role partners with data scientists and Marketing stakeholders to translate requirements into reliable data solutions, including data ingestion, quality controls, and model-ready datasets.

Qualifications

• Bachelor’s degree in a technical field is required.
• Hands-on leadership of technical projects or small teams desired.
• Strong SQL and Python proficiency for production-grade pipelines.

Responsibilities

• Build scalable marketing data pipelines in Snowflake/Databricks and other cloud environments.
• Develop API integrations and ingestion frameworks for CRM, marketing automation, and analytics data.
• Design canonical data models and shared datasets across accounts, leads, campaigns, and opportunities.
• Engineer ML-ready datasets with time-aware features and training/inference data.
• Lead delivery, review designs, mentor engineers, and improve development practices.

Skills

SQL

Python

Spark

API integrations

Data modeling

MLOps

CI/CD

Data quality

Mentoring engineers

Education

Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical field

Tools

Snowflake

Databricks

dbt

Airflow

Job description

Advanced Micro Devices, Inc. is seeking a hands-on Manager of Marketing Data Engineering within the Marketing Analytics team.

You will build scalable data pipelines, canonical datasets, and trusted data assets enabling advanced analytics, ML, and campaign optimization. The role partners with data scientists and Marketing stakeholders to translate requirements into reliable data solutions, including data ingestion, quality controls, and model-ready datasets.