Staff Data Engineer

Advanced Micro Devices, Inc • Austin, TX

21h

About The Position

AMD is seeking a Staff Data Engineer for its Marketing Analytics team to build and operationalize the data foundation supporting advanced analytics, data science, and machine learning across Marketing. The role leads hands-on development of scalable pipelines, canonical data models, and trusted, reusable datasets for use cases such as lead and account scoring, attribution, segmentation, forecasting, and campaign optimization.

The engineer collaborates with data scientists, analytics professionals, Marketing stakeholders, and enterprise technology teams to turn analytical requirements into reliable data solutions. Work includes integrating internal and external data through APIs and other ingestion methods; engineering datasets in Snowflake, Databricks, or comparable environments; and establishing workflows, quality controls, and documentation for repeatable model development and ongoing machine learning operations.

The ideal candidate is a technically strong, hands-on engineering leader who builds reusable data assets with consistent definitions, clear relationships, and reliable historical context. They are comfortable with complex, fragmented data and balance near-term delivery with maintainability and scale. The role also calls for cross-functional collaboration, clear communication of trade-offs, and the ability to mentor engineers while remaining involved in solution design and development.

Requirements

• Experience in data engineering, analytics engineering, data platform development, or a related discipline, including leading technical projects or small engineering teams.
• Strong hands-on proficiency in SQL and Python and experience developing production-grade data pipelines, transformations, and reusable engineering components.
• Hands-on experience with Snowflake, Databricks, or comparable cloud data platforms, including data modeling, workload optimization, orchestration, and scalable analytical datasets.
• Demonstrated experience building API-based data integrations, including authentication, pagination, rate limits, incremental extraction, retry logic, and schema evolution.
• Experience preparing data for machine-learning and data-science workflows, including feature engineering pipelines, label generation, point-in-time-correct datasets, training and inference consistency, and reproducible dataset creation.
• Familiarity with modern data engineering and MLOps practices, including workflow orchestration, Git-based version control, automated testing, CI/CD, environment management, and data requirements for model deployment and retraining.
• Experience designing canonical data models and reusable data products, including entity resolution, deduplication, consistent data grain, historical tracking, and integration of structured and semi-structured data across systems.
• Understanding of marketing and sales data ecosystems, including CRM, marketing automation, digital analytics, paid media, account-based marketing, events, and opportunity data.
• Strong collaboration and communication skills, including translating business and analytical needs into engineering requirements, explaining technical trade-offs, coordinating cross-functional delivery, and mentoring technical colleagues.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical field.
• This role is not eligible for visa sponsorship.

Nice To Haves

• Experience with Spark or PySpark.
• Familiarity with feature stores and dataset versioning.
• Experience with tools such as dbt or Airflow.
• A master’s degree or equivalent advanced experience.

Responsibilities

• Build and operate scalable marketing data pipelines that transform internal and external source data into reliable, analysis-ready and machine-learning-ready datasets.
• Develop reusable API integrations and ingestion frameworks for CRM, marketing automation, digital analytics, media, events, partners, and other sources, with controls for incremental updates, schema changes, failures, and reprocessing.
• Design and maintain canonical datasets and shared data models with consistent definitions, identifiers, relationships, and historical views across core marketing entities.
• Partner with data scientists to engineer machine-learning datasets, including features, labels, and training, validation, testing, and inference datasets; use historical snapshots and point-in-time logic to support reproducibility and prevent data leakage.
• Lead hands-on engineering delivery and continuous improvement, including managing priorities and dependencies, reviewing technical designs and code, mentoring engineers, and improving practices through reusable components, automated testing, and disciplined release management.
• Establish data quality, observability, and operational reliability through automated validation, pipeline monitoring, freshness checks, and controls for schema and data-distribution changes.
• Troubleshoot data pipeline issues and improve performance, recovery processes, and compute efficiency.
• Partner with Marketing, Analytics, Data Science, Sales Operations, and IT to understand priorities, translate use cases into data requirements, and align on dataset definitions, delivery expectations, and ownership.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior