3 days ago
Base Salary
$116k - $173k/yr
Responsibilities
- Build and operate scalable marketing data pipelines that transform internal and external data into reliable, analysis-ready, and machine-learning-ready datasets.
- Develop reusable API integrations and ingestion frameworks with controls for incremental updates, schema changes, failures, retries, rate limits, and reprocessing.
- Design and maintain canonical datasets and shared data models for accounts, contacts, leads, campaigns, engagement, spend, and opportunities.
- Partner with data scientists to create features, labels, and training, validation, testing, and inference datasets using historical snapshots and point-in-time logic.
- Lead hands-on engineering delivery, review technical designs and code, manage priorities and dependencies, mentor engineers, and improve development and release practices.
- Establish data quality, observability, monitoring, freshness checks, schema and data-distribution controls, performance improvements, recovery processes, and compute efficiency.
- Collaborate with Marketing, Analytics, Data Science, Sales Operations, and IT to define requirements, dataset ownership, delivery expectations, and business-aligned data solutions.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical field.
- Hands-on proficiency in SQL and Python and experience building 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 integrations involving authentication, pagination, rate limits, incremental extraction, retry logic, schema evolution, and other ingestion approaches.
- Experience preparing data for machine learning and data science, including feature engineering pipelines, label generation, point-in-time-correct datasets, training and inference consistency, and reproducible dataset creation.
- Familiarity with feature stores, dataset versioning, MLOps, workflow orchestration, Git-based version control, automated testing, environment management, model deployment, and retraining requirements.
- Familiarity with Spark or PySpark and tools such as dbt or Airflow is preferred.
- Experience designing canonical data models and reusable data products, including entity resolution, deduplication, consistent data grain, historical tracking, and structured and semi-structured data integration.
- 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 trade-offs, coordinating delivery, and mentoring technical colleagues.
- Experience leading technical projects or small engineering teams is preferred.
- A master’s degree or equivalent advanced experience is preferred.
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.