Data Engineer

Position: Staff Data Engineer
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THE ROLE:

AMD is seeking a Staff Data Engineer within the Marketing Analytics team to build and operationalize the data foundation that supports advanced analytics, data science, and machine learning across Marketing. This person will lead the hands-on development of scalable data pipelines, canonical data models, and trusted, reusable datasets that enable analytical use cases—from lead and account scoring to attribution, segmentation, forecasting, and campaign optimization.

The role will work closely with data scientists, analytics professionals, Marketing stakeholders, and enterprise technology teams to translate analytical requirements into reliable data solutions. Responsibilities include integrating internal and external data through APIs and other ingestion methods, engineering datasets within environments such as Snowflake or Databricks, and establishing the data workflows, quality controls, and documentation needed to support repeatable model development and ongoing machine learning operations.

THE PERSON:

The ideal candidate is a hands-on data engineering leader who combines strong technical execution with an understanding of how data supports analytics and machine learning. They think beyond individual pipelines to build reusable data assets with consistent definitions, clear relationships, and reliable historical context. They must be comfortable working with complex, fragmented data, translating analytical needs into practical engineering requirements, and balancing immediate delivery needs with long-term maintainability and scale.

They must also be a collaborative problem-solver who builds trusted relationships across business and technical teams, communicates trade-offs clearly, and brings discipline to data quality, documentation, and delivery. The ability to guide and mentor engineers while remaining closely involved in solution design and development is important to success in this role.

KEY RESPONSIBILITIES:

Build and operate scalable marketing data pipelines, transforming data from internal platforms and external sources into reliable, analysis-ready and machine-learning-ready datasets within Snowflake, Databricks, or comparable cloud data environments.

Develop reusable API integrations and ingestion frameworks to bring together CRM, marketing automation, digital analytics, media, event, partner, and other relevant data, with appropriate controls for incremental updates, schema changes, failures, and reprocessing.

Design and maintain canonical datasets and shared data models, establishing consistent definitions, identifiers, relationships, and historical views across core marketing entities such as accounts, contacts, leads, campaigns, engagement, spend, and opportunities.

Engineer datasets for machine learning and data science, partnering with data scientists to develop features, labels, and training, validation, testing, and inference datasets. Apply appropriate historical snapshots and point-in-time logic to support reproducibility and prevent data leakage.

Lead hands-on engineering delivery and continuous improvement, managing priorities and dependencies, reviewing technical designs and code, mentoring engineers, and improving…