Senior Data Engineer, Growth Acquisition (Growth & Marketing Data)

San Mateo, CA · San Mateo · USD 243,290-295,250 / year · 8+ years experience · Full-time · Onsite · Posted Oct 1, 2026

At a glance

The Senior Data Engineer for Growth & Marketing Data will build the data foundation for marketing measurement and the growth flywheel.
This role involves developing pipelines and models for Marketing Mix Modeling (MMM), attribution, and end-to-end funnel analytics.
The engineer will work closely with Marketing, Data Science, and Product teams to ensure data quality and support long-term retention strategies.

Design, build, and operate batch and streaming pipelines unifying marketing data with in-platform user behavior.
Own the data foundation for MMM by building versioned, analysis-ready datasets.
Partner with Marketing and Data Science to define shared metrics including acquisition, activation, retention, and LTV.
Architect event taxonomies and logging standards for acquisition and lifecycle events.
Integrate with ad channel platforms, mobile measurement partners, and third-party sources.
Set standards for table design, data quality monitoring, observability, lineage, and documentation.
Support experimentation and incrementality work such as geo tests, holdouts, and lift studies.
Mentor other engineers and collaborate with Data Engineering, Data Infrastructure, and ML Platform teams.
Ensure measurement approaches respect user privacy and safety.

8+ years of industry experience as a data engineer or data-focused software engineer.
Experience building scalable batch and streaming pipelines using SQL and Python, PySpark, or Scala.
Experience with orchestration tools like Airflow or Dagster.
Experience with large-scale data processing using Spark, Flink, or Kafka for multi-terabyte datasets.
Strong data modeling skills for dimensional or event-based models.
Experience working directly with business and data science partners to build data products.
Experience with at least one major cloud platform (AWS, GCP, or Azure) and modern warehouse or lakehouse technologies.
Comfort with software practices including code review, CI/CD, testing, and monitoring.

Hands-on exposure to marketing measurement such as MMM (Robyn, Meridian, PyMC-Marketing), attribution, or incrementality platforms.
Experience with marketing and ad data sources like ad platform APIs, mobile measurement partners, or CRM tooling.
Experience with identity resolution.
Experience with data privacy constraints in measurement such as consent, aggregation, clean rooms, or differential privacy.
Experience in consumer products with a focus on growth, ads, personalization, or lifecycle.