Data Engineering Manager

AMD; Santa Clara (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

AMD is seeking a Manager of Marketing Data Engineering to lead the data foundation for analytics, data science, and ML across Marketing. You will build scalable pipelines, canonical datasets, and trusted data assets enabling lead scoring, attribution, segmentation, forecasting, and campaign optimization.

You will collaborate with data scientists and business stakeholders to translate requirements into reliable data solutions, integrate internal and external data via APIs, and establish robust

Qualifications

• Strong hands-on proficiency in SQL and Python for production-grade data pipelines.
• Experience designing data models and canonical datasets for analytics.
• Experience leading technical projects or small engineering teams is a plus.

Responsibilities

• Build and operate scalable marketing data pipelines and datasets in Snowflake/Databricks.
• Develop reusable API integrations for CRM, marketing automation, and analytics.
• Design canonical datasets and data models across accounts, leads, campaigns, and opportunities.
• Engineer datasets for ML/DS with proper training/validation/inference sets.
• Lead delivery, mentor engineers, and improve release practices.

Skills

SQL

Python

Spark

Snowflake

Databricks

Data pipelines

Tools

APIs

ETL frameworks

Job description

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believe technology has the power to solve the world's most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.

Whether you're designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger - technology that moves the world forward. Join us and, together, we'll advance your career.

THE ROLE:

AMD is seeking a Manager of Marketing Data Engineering 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 development practices through reusable components, automated testing, and disciplined release management.
• Establish data quality, observability, and operational reliability, implementing automated validation, pipeline monitoring, freshness checks, and controls for schema and data distribution changes. Troubleshoot issues and improve pipeline performance, recovery processes, and compute efficiency.
• Partner with Marketing, Analytics, Data Science, Sales Operations, and IT to understand business priorities, translate use cases into data requirements, and align on dataset definitions, delivery expectations, and ownership.

PREFERRED EXPERIENCE:

• Experience in data engineering, analytics engineering, data platform development, or a related discipline, including experience leading technical projects or small engineering teams.
• Strong hands‑on proficiency in SQL and Python, with experience developing production‑grade data pipelines, transformations, and reusable engineering components. Experience with Spark or PySpark is a plus.
• Hands‑on experience with Snowflake, Databricks, or comparable cloud data platforms, including data modeling, workload optimization, orchestration, and management of