below marketmost similar roles pay hereabove market
This role pays less than
52%
of similar roles. Most pay
$149,375–$223,750
— the blue band above.
At the midpoint, this role pays about
$188k
versus about
$187k
for comparable roles.
Based on 240 similar postings.
Employer
About Amd
AMD (Advanced Micro Devices) is a semiconductor company that develops high-performance processors, graphics cards, and adaptive computing solutions for gaming, data centers, and embedded markets. Industry: Semiconductors
Amd currently has
496 open roles
on FindRole.
Listed pay typically runs
$172,000–$258,000
across 496 roles with salary data.
The Staff Data Engineer joins the Marketing Analytics team to build and operationalize a robust data foundation for advanced analytics, data science, and machine learning. This role involves leading the hands-on development of scalable data pipelines, canonical data models, and reusable datasets to support use cases like lead scoring, attribution, and campaign optimization. You will integrate internal and external data via APIs into environments like Snowflake or Databricks. Key responsibilities include engineering datasets for machine learning, establishing data quality controls, and managing workflows for CRM, marketing automation, and digital analytics data. Required skills include proficiency in SQL, Python, and Spark or PySpark. You must demonstrate experience with data modeling, workflow orchestration, and MLOps practices to solve complex problems involving fragmented marketing and sales data ecosystems.
What does a Data Engineer earn in California?
Median $187700 from 41 postings across 21 companies.
Build and operate scalable marketing data pipelines within Snowflake or Databricks to create analysis-ready datasets.
Develop reusable API integrations and ingestion frameworks for CRM, marketing automation, and digital analytics data.
Design and maintain canonical datasets and shared data models with consistent definitions for core marketing entities.
Engineer specific datasets for machine learning, including features, labels, and point-in-time logic for model reproducibility.
Lead hands-on engineering delivery by reviewing technical designs, mentoring engineers, and managing development priorities.
Establish data quality and observability through automated validation, pipeline monitoring, and freshness checks.
Translate business priorities from marketing and sales stakeholders into technical data requirements and engineering specifications.
What we're looking for
Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical field.
Master’s degree or equivalent advanced experience (preferred).
Experience in data engineering, analytics engineering, data platform development, or a related discipline, including leading technical projects or small engineering teams (preferred).
Strong hands-on proficiency in SQL and Python for developing production-grade data pipelines and transformations (preferred).
Hands-on experience with Snowflake, Databricks, or comparable cloud data platforms, including data modeling and workload optimization (preferred).
Experience building API-based data integrations and preparing data for machine learning and data science workflows (preferred).
Familiarity with MLOps, workflow orchestration, Git-based version control, and tools such as dbt or Airflow (preferred).
Experience designing canonical data models and understanding marketing and sales data ecosystems (preferred).