Sr Director - Enterprise Data Architect, Advertiser Intelligence Platform

USA TODAY Co.

31d•$175,000 - $180,000•Remote

About The Position

USA TODAY is seeking a Sr Director - Enterprise Data Architect to lead the strategy and hands-on delivery of a next-generation Advertiser Intelligence Platform. This leader will build the trusted data and AI foundation that enables unified advertiser profiles, peer benchmarking, opportunity intelligence, predictive insights, and workflow integration across sales, customer success, and operations. This is a high-impact leadership role for a builder who can define the strategy and also execute. The platform vision is rooted in creating trusted identity and data capabilities that power advertising, subscriptions, commerce and loyalty, personalization, experiences, and stronger monetization across a connected platform.

Requirements

15+ years in enterprise data architecture, data platform engineering, analytics architecture, or closely related leadership roles, including ownership of complex cross-functional data programs.
Proven experience designing enterprise-scale data architectures that unify data from multiple operational and platform systems into trusted, governed, and production-ready domain models.
Demonstrated success building or modernizing advertiser, customer, identity, martech, adtech, subscription, or revenue intelligence platforms.
Strong experience with cloud data platforms and large-scale analytical ecosystems, ideally including BigQuery, modern orchestration patterns, semantic modeling, and near-real-time or daily refresh pipelines.
Practical experience designing AI/ML-enabled data products, including feature design, scoring frameworks, recommender patterns, similarity matching, vector search, or predictive analytics capabilities.
Experience translating ambiguous business goals into phased roadmaps, MVPs, architecture standards, and shipped capabilities adopted by business users.
Experience partnering directly with sales, customer success, operations, finance, and product leaders to convert data strategy into business impact.
Track record leading hands-on architecture reviews, mentoring engineers and architects, and unblocking execution in delivery-critical moments.
Deep enterprise data architecture experience in complex, cross-functional environments.
Proven success building production-grade data and intelligence platforms.
Strong background in cloud data ecosystems, modern analytics, and AI/ML-enabled architecture.
Ability to lead at executive altitude and execute at implementation depth.
Strong communication, influence, and operating discipline.

Responsibilities

Define the enterprise architecture and phased roadmap for the Advertiser Intelligence Platform.
Build the golden advertiser profile strategy and canonical data model.
Architect AI enrichment, opportunity scoring, similarity matching, and predictive intelligence capabilities.
Partner with engineering and product teams to deliver MVP through scale.
Integrate insights into CRM, BI, and business workflows.
Establish governance for data quality, privacy, lineage, explainability, and cost control.
Work directly with GTM and operations leaders to ensure the platform delivers measurable business results.
Define the end-to-end architecture for USA TODAY’s Advertiser Intelligence Platform, aligned to the business goal of turning advertiser data into revenue growth, retention improvement, and faster decision-making.
Establish the multi-phase architecture spanning golden advertiser profiles, AI enrichment, similarity matching, intelligence UI, predictive analytics, and AI as a central integration layer.
Translate business goals into a pragmatic implementation roadmap that starts small, validates core mechanics, and scales responsibly.
Create architectural standards for trust, lineage, freshness, transparency, explainability, privacy, and operational resilience.
Design the canonical advertiser domain model and golden profile architecture across identifiers, business profile, lifecycle stage, seasonality, product portfolio, financial metrics, performance metrics, creative intelligence, engagement history, geographic footprint, competitive positioning, and data freshness metadata.
Define normalized schemas and data products for advertiser core profile, products, financial metrics, platform performance, temporal metrics, lifecycle history, seasonality profiles, creative inventory, freshness indicators, opportunity packs, and churn-risk signals.
Architect data ingestion, harmonization, identity resolution, deduplication, quality controls, and lineage patterns across the data lake and operational platforms.
Ensure the platform supports lifecycle-normalized and seasonality-normalized comparisons so commercial teams can trust the intelligence surfaced to them.
Lead the design of AI-enriched advertiser intelligence, including profile health scoring, opportunity scoring, tiered enrichment, online presence research, competitive intelligence, and evidence-pack generation.
Architect vectorized advertiser representations and peer matching patterns using embedding features such as vertical, spend trajectory, product mix, campaign mix, creative diversity, geography, performance, seasonality, and lifecycle stage.
Partner with data science and engineering teams to define prediction and recommendation patterns for churn, expansion, product adoption, and ROI impact modeling, with clear confidence labeling and non-causal positioning where appropriate.
Ensure AI is implemented as a decision-support capability for business teams, not as an uncontrolled automation layer.
Lead discovery, architecture definition, MVP design, phased delivery, and production rollout for the platform.
Personally review and contribute to schemas, interface contracts, orchestration logic, data quality rules, and implementation plans.
Guide teams through pilot execution, manual validation with sales teams, feedback loop instrumentation, and post-MVP expansion.
Drive integration of platform insights into operational tools such as CRM, BI, email workflows, and collaboration channels.
Establish governance for enrichment costs, token and API budgets, cooldown rules, privacy compliance, source transparency, and quarterly ROI reviews.
Define controls for data quality, misleading peer comparisons, confidence labeling, and research-source compliance.
Build an operating model for continuous improvement using user feedback, closed-loop outcomes, and weekly or daily data refresh patterns.
Act as the senior technical voice across executives, product, engineering, commercial operations, and GTM stakeholders.