28 days ago
Santa Clara, CA, USAStaff+
Responsibilities
- Build and operate scalable marketing data pipelines that produce analysis-ready and machine-learning-ready datasets.
- Develop reusable API integrations and ingestion frameworks with controls for incremental updates, schema changes, failures, retries, and reprocessing.
- Design and maintain canonical datasets and shared data models for marketing entities including accounts, contacts, leads, campaigns, engagement, spend, and opportunities.
- Engineer features, labels, training, validation, testing, and inference datasets for data science and machine learning workflows.
- Lead hands-on technical delivery, review designs and code, mentor engineers, and improve reusable components, testing, and release practices.
- Establish data quality, observability, monitoring, freshness checks, schema-change controls, troubleshooting, recovery processes, and compute-efficiency improvements.
- Partner with Marketing, Analytics, Data Science, Sales Operations, and IT to translate business needs into data requirements and align on dataset ownership and delivery.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical field.
- Master’s degree or equivalent advanced experience is preferred.
- Experience in data engineering, analytics engineering, data platform development, or a related discipline, including leadership of 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.
- Experience with Snowflake, Databricks, or comparable cloud data platforms, including data modeling, workload optimization, orchestration, and scalable analytical datasets.
- Experience building API-based data integrations and familiarity with file-based and other ingestion approaches.
- Experience preparing data for machine learning and data science workflows, including feature engineering, label generation, point-in-time-correct datasets, and reproducible dataset creation.
- Familiarity with feature stores, dataset versioning, MLOps, workflow orchestration, Git-based version control, automated testing, CI/CD, and environment management.
- Experience designing canonical data models and reusable data products, including entity resolution, deduplication, historical tracking, and structured and semi-structured data integration.
- Understanding of marketing and sales data ecosystems such as CRM, marketing automation, digital analytics, paid media, account-based marketing, events, and opportunity data.
- Strong collaboration and communication skills, including the ability to explain technical trade-offs, coordinate cross-functional delivery, and mentor technical colleagues.
- Experience with Spark or PySpark, dbt, or Airflow is a plus.
Tech Stack
Categories
Data Engineering
About AMD
AMD designs and sells CPUs, GPUs, and adaptive/embedded computing products for PCs, data centers, gaming, and edge devices. Its portfolio includes Ryzen and EPYC processors, Radeon and Instinct graphics, and adaptive SoCs from its Xilinx acquisition, sold to OEMs, cloud providers, and device makers. Founded in 1969 and headquartered in Santa Clara, it is a public company on NASDAQ and supplies semi-custom chips for major game consoles.