Data Engineering Manager - CA, Santa Clara, United States

Originally posted 14 October 2026 by the employer.

Build and operationalize the data foundation that supports advanced analytics, data science, and machine learning across Marketing.

About the role

This role involves building and operationalizing the data foundation that supports advanced analytics, data science, and machine learning across Marketing, leading the hands-on development of scalable data pipelines, canonical data models, and trusted, reusable datasets. This includes integrating internal and external data, engineering datasets within environments such as Snowflake or Databricks, and establishing data workflows and quality controls.

What you'll do

• Build and operate scalable marketing data pipelines, transforming data from internal platforms and external sources into analysis-ready and machine-learning-ready datasets within Snowflake, Databricks, or comparable cloud data environments.
• Develop reusable API integrations and ingestion frameworks for CRM, marketing automation, digital analytics, media, event, and partner data.
• Design and maintain canonical datasets and shared data models, establishing consistent definitions, identifiers, relationships, and historical views across core marketing entities.
• Engineer datasets for machine learning and data science, partnering with data scientists to develop features, labels, and training, validation, testing, and inference datasets.
• Lead hands-on engineering delivery and continuous improvement, managing priorities and dependencies, reviewing technical designs and code, and mentoring engineers.
• Establish data quality, observability, and operational reliability, implementing automated validation, pipeline monitoring, freshness checks, and controls.

What you'll need

• 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.
• Hands-on experience with Snowflake, Databricks, or comparable cloud data platforms, including data modeling, workload optimization, orchestration, and management of 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 MLOps and modern data engineering practices, including workflow orchestration, Git-based version control, automated testing, CI/CD, and environment management.

Nice to have

• Experience with Spark or PySpark.
• Familiarity with feature stores and dataset versioning.
• Experience with tools such as dbt or Airflow.
• Experience designing canonical data models and reusable data products.
• Understanding of marketing and sales data ecosystems, including CRM, marketing automation, digital analytics, paid media, account-based marketing, events, and opportunity data.
• A master’s degree or equivalent advanced experience.

Skills: Marketing Data Engineering, scalable data pipelines, canonical data models, Snowflake, Databricks