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Senior Analytics Engineer, Marketing Sciences

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Salary

$160,000 - $200,000

Location

United States

Posted

Yesterday

We are looking for a Senior Data Engineer to lead the evolution of our Marketing Sciences data ecosystem. In this role, you will be the technical architect of the infrastructure that powers our growth engine—from performance marketing attribution to the productionalization of predictive models.

You will bridge the gap between core data engineering and high-impact growth initiatives. While our Data Science team focuses on R&D, you will ensure the high-fidelity customer signals required for Next Best Action (NBA) frameworks, Predictive Lifetime Value (pLTV) models, and Demand Modeling are accurate, scalable, and readily available for the Growth Analytics team.

Responsibilities

  • Growth Data Pipeline Architecture: Build and maintain scalable systems to ingest data from a wide range of sources, specifically focusing on marketing platforms (Google Ads, Meta), CRM data, and event streams.
  • Predictive Model Enablement: Partner with the Growth Analytics team to move predictive models (like pLTV and Churn risk) from research into production-ready data environments.
  • Attribution & Demand Modeling: Architect the technical pipelines for Demand Models and LTV:CAC reporting, ensuring company-wide alignment through standardized metrics.
  • Experimental Infrastructure: Support the design and execution of A/B tests and causal inference models by ensuring clean, reliable experiment logging and cohort assignment.
  • MarTech Integration: Work closely with the MarTech team to optimize our stack and ensure "Source of Truth" consistency across platforms like Iterable, Hubspot, Gong, Segment and more
  • Reverse ETL & Activation: Enable "Next Best Action" use cases by syncing data warehouse insights back into product and marketing tools for real-time customer interventions.

Desired skills & experience

  • Modern Data Stack: Expert proficiency in Snowflake and DBT for creating modular, testable transformations that support complex growth analytics.
  • Programming & Scripting: Proficient in Python and SQL, with an "early adopter" mindset toward leveraging modern coding environments and automation to accelerate output.
  • Marketing Data Savvy: Proven experience handling semi-structured API data (JSON) from marketing sources and managing the complete lifecycle from ingestion to consumption.
  • Segment: Expertise in designing, implementing, and managing Segment event pipelines.
  • Strategic Architecture: Ability to lead a technical vision with a holistic view of how data engineering impacts business-critical metrics like LTV, CAC, and ROAS.
  • Collaboration: Experience working in an Agile (SCRUM/Kanban) environment, partnering closely with Analysts, Data Scientists, and Marketing Stakeholders.

Bonus points

  • Experience with Reverse ETL tools (e.g., Census, Hightouch).
  • Familiarity with Real-time ETL (Kafka streaming, AWS Kinesis).
  • AWS DevOps experience (Terraform, Kubernetes, Docker)

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