VP, Data Engineering

dentsu•New York, NY

3d•Hybrid

About The Position

You will lead the vision, strategy, and execution of our enterprise data ecosystem as Vice President of Data Engineering. In this role, you’ll shape how data is built, governed, and scaled across the organization—enabling advanced analytics, AI/ML, and data-driven decision-making. You’ll partner with cross-functional leaders in product, engineering, analytics, security, and compliance to deliver a trusted, scalable, and high-performing data platform. Your work will directly impact how the organization leverages data to drive innovation and growth.

Requirements

Significant experience leading data engineering, data architecture, or platform engineering efforts.
Led teams and initiatives at scale, with a focus on collaboration and results.
Strong experience building and operating cloud-based data platforms (such as GCP, Azure, or similar environments).
Worked with modern data technologies, including data warehouses, pipelines, and distributed processing systems.
Understand data governance, data quality, and responsible data management practices.
Experience driving modernization, migration, or transformation initiatives.
Skilled at influencing stakeholders and communicating complex ideas clearly.

Responsibilities

Set Strategy & Lead Teams: Define and evolve the enterprise data engineering strategy aligned with business goals and transformation priorities. Lead, coach, and grow inclusive, high-performing teams across data engineering, architecture, and governance. Foster a culture grounded in quality, accountability, and continuous improvement. Partner with senior leaders to prioritize investments and drive aligned outcomes.
Build Data Governance & Trust: Establish and advance enterprise-wide data governance frameworks, standards, and practices. Ensure clear data ownership, lineage, metadata management, and stewardship. Guide practices that support data privacy, security, and regulatory compliance. Improve data quality, discoverability, and lifecycle management so teams can confidently use data.
Design & Scale Data Platforms: Architect and oversee scalable, cloud-based data platforms (GCP, Azure, Snowflake). Guide the development of data warehouses, data lakes, pipelines, and streaming solutions. Ensure systems are reliable, secure, high-performing, and resilient. Advance automation, CI/CD, and infrastructure-as-code practices to increase efficiency.
Drive Modern Data Capabilities: Lead adoption of modern data technologies and frameworks. Enable seamless data ingestion, transformation, and orchestration across diverse data sources. Optimize performance and cost across platforms like Snowflake. Evaluate and introduce new capabilities supporting AI/ML, real-time analytics, and self-service data.