Senior Director, Marketing Data Services (Owned & Paid)

Position Overview

The Senior Director, Marketing Data Services owns the vision, roadmap and adoption of governed data services for Owned and Paid marketing on Marriott’s Data Services Platform. These services cover audiences, activation, contact pressure, media performance and measurement.

This is a technical product leadership role and has a strong grounding in data engineering and architecture concepts, enough to frame technical requirements, evaluate design trade-offs, and bring teams up to speed on Marriott’s technologies. They turn Owned and Paid marketing needs into clear product requirements and roadmaps for reusable data services on Snowflake, AWS and GCP, and serve as the main data partner to marketing stakeholders. As a senior leader within Enterprise Data, Analytics & AI, the role works as one team with the Digital Foundations pod and platform engineering, so marketing data is built once, governed consistently, and ready for AI and real-time decisioning.

Qualifications

Required

• 12+ years in data product management, data strategy or data architecture, including 5+ years leading teams. A foundation in data engineering or analytics engineering is strongly preferred.

• Proven experience owning data products or platforms end to end: vision, roadmap, requirements, prioritization and adoption.

• Strong grasp of data engineering concepts, including pipelines, data modeling, data contracts, data quality and orchestration, and the ability to turn business needs into clear technical requirements.

• Working knowledge of Snowflake and modern data tooling (e.g., dbt, Airflow). SQL proficiency to explore data and validate solutions.

• Cloud: Strong understanding of AWS and GCP data services and architecture patterns, enough to frame requirements and weigh cost, security and scalability trade-offs with engineering.

• Feature and vector stores: Understanding of how enterprise feature stores and vector stores are designed and used, and the ability to define requirements for them in ML and AI use cases such as marketing mix modeling (MMM).

• Graph, NoSQL and distributed databases: Familiarity with graph, NoSQL and distributed databases (e.g., Neo4j or Neptune, DynamoDB, MongoDB or Bigtable) and when each fits better than the warehouse.

• Experience defining semantic layers, metric layers or certified KPI frameworks.

• Experience with marketing data: CRM and lifecycle, paid media, audiences, attribution and spend.

• Working knowledge of martech and adtech, such as customer data platforms (e.g., Adobe Experience Platform), email/CRM platforms, search and programmatic platforms, and clean rooms.

• Strong understanding of privacy, consent and data governance in marketing activation.

• Proven record of setting direction and building trust across organizational lines, and aligning business and technology teams on consistent, scalable approaches.

• Ability to explain technical concepts clearly to senior business stakeholders, and to explain business priorities clearly to engineers.

• Bachelor’s degree in Computer Science, Engineering, Information Systems or a related quantitative field required; advanced degree preferred.

Preferred

• Prior hands-on experience as a data engineer, analytics engineer or data architect earlier in their career.

• Familiarity with streaming and event-based data (e.g., Kafka) and API-based data services.

• Exposure to Snowflake AI capabilities (e.g., Cortex), ontologies, and data for AI agents.

• Applied GenAI experience, such as defining AI use cases, data requirements for agents, or LLM-based products in production.

• Experience in-housing paid media data or building MMM and incrementality data foundations.

• Hospitality, travel or loyalty industry experience.

Key Responsibilities

Data Strategy & Architecture

• Own the Owned and Paid marketing data services roadmap within Marriott’s future-state Data Services Platform, including identity and consent, audience and activation, paid media connectors, and the measurement feedback loop.

• Shape the target data architecture for marketing data in partnership with enterprise architecture and engineering, including source ingestion, medallion layers, data products and how consumers access them.

• Lead the in-housing and centralization of paid media data, covering spend, impressions and outcomes from publishers and DSPs.

• Drive measurement foundations for attribution, incrementality and MMM in partnership with Marketing & Digital Analytics.

Technical Requirements & Solution Framing

• Define product requirements, data contracts and acceptance criteria for marketing data services, including audiences and holdouts, contact history and contact pressure, media performance, and conversion and attribution.

• Partner with engineering and architecture to evaluate design options across Snowflake, AWS and GCP, making informed trade-offs on cost, speed, scalability and reuse.

• Define requirements for the enterprise feature store and vector store that support MMM, incrementality and AI use cases.

• Define certified marketing metrics, entities and relationships in the semantic layer, so BI tools, analysts and AI agents work from the same definitions.

• Bring product and engineering teams up to speed on Marriott’s data technologies and standards, and translate them into practical delivery approaches.

Marketing Stakeholder Partnership

• Serve as the primary data partner to Owned (CRM, lifecycle, global promotions, personalization) and Paid (search, programmatic, social, RIOTT) marketing leaders.

• Turn business questions into product requirements, priorities and delivery plans, and explain technical trade-offs in business terms.

• Represent marketing data services in enterprise forums and with agency and platform partners.

One Team with Digital Foundations

• Co-own marketing and traffic-source taxonomy, campaign attribution and semantic layer deliverables with the Digital Foundations pod.

• Run a joint backlog and shared success measures that connect digital behavior data and marketing data to bookings, revenue and loyalty.

• Work with Marketing & Digital Data Technology and platform teams, who own engineering standards, environments and deployment.

Team Leadership & Delivery

• Lead a blended onshore/offshore team of data product analysts, data and quality engineers, data modelers and taxonomy specialists.

• Set the bar for requirements quality, documentation and reuse, and build data and technical literacy across the team.

• Measure investments against business outcomes: attributable revenue, media efficiency, speed to insight and analyst effort.

• Ensure solutions meet privacy, consent and data governance standards.

Success in the First 12 Months

• Owned and Paid data services delivered on shared, reusable foundations, with no new one-off pipelines.

• A joint taxonomy and semantic layer roadmap with Digital Foundations, delivered against shared success measures.

• Clear, engineering-ready requirements that measurably reduce time from marketing request to usable data.

• Marketing stakeholders see this person as their go-to data partner, and engineering partners see them as a trusted product owner.