VP, Data Architecture & Solutions

dentsu•New York, NY

1d•Hybrid

About The Position

Role Summary Reports to: EVP, Data Experience Help build the foundational data infrastructure that powers everything Dentsu’s Decisioning practice delivers. In this role, you will scale our proven Azure/Databricks/Python/PySpark stack across client engagements and design the architecture that supports our agentic data systems, third‑party analytics partners, and AI orchestration tools. This is an architecture‑first leadership role: you create the patterns, your team builds the platform, and every data product in the practice relies on what you design. Your focus is taking what works and making it consistently scalable across the organization.

Requirements

10+ years of experience in data engineering and architecture with increasing leadership responsibilities.
Deep expertise in Databricks, Apache Spark, and modern lakehouse architecture.
Strong experience with the Azure ecosystem (including ADLS and ADF).
Demonstrated experience building and leading data engineering teams (5+ direct or indirect reports).
Expert knowledge of PySpark, data modeling, and enterprise‑scale transformation frameworks.
Experience designing multi‑tenant data platforms supporting multiple clients or business units.
Strong understanding of data governance, data quality frameworks, and taxonomy management.
Ability to communicate clearly with both technical and non-technical stakeholders.

Nice To Haves

Experience in media agency or advertising technology environments.
Familiarity with Adverity or similar data integration platforms.
Experience with Power BI at enterprise scale (semantic models, governance, deployment pipelines).
Background scaling a proven single‑client stack into a multi‑client or multi‑agency model.
Experience managing offshore engineering teams.
Knowledge of media data sources (CM360, SA360, Meta, TTD, programmatic exchanges).

Responsibilities

Own the enterprise data architecture strategy across Azure, Databricks, ADLS, PySpark, and Power BI—defining reference patterns used across client engagements.
Lead a team of seven (3 onshore engineers, 3 DGS engineers, and 1 Director, Staff Data Engineer), setting clear technical standards and architecture patterns.
Design and optimize the data foundation powering Genie spaces and agentic workflows, ensuring AI systems have fast, governed access to trusted data across Databricks and ADLS.
Ensure the data foundation is optimized for agentic consumption by partnering with the AI Agents pillar to define data contracts, access patterns, and performance requirements across Search, Social, and Programmatic channels.
Define reusable pipeline patterns and data quality frameworks that scale across Dentsu X, iProspect, and Carat while supporting agency‑specific needs.
Partner with Data Science to support data requirements for MMM, RBA, incrementality, and forecasting models.
Own and maintain cross‑agency taxonomy standards, including naming conventions, data contracts, and the metadata layer enabling unified reporting and AI consumption.
Manage cloud infrastructure costs across Azure and Databricks, optimizing compute, storage, and pipeline spend while meeting performance SLAs.
Recruit and develop platform engineers who build for reuse and think in systems.
Establish and maintain data governance and security standards, including PII handling, access controls, and compliance frameworks across client environments.