Senior Vice President, Data Platform

Dotdash Meredith•New York, NY

4d•$315,000 - $375,000•Hybrid

About The Position

We are seeking a Senior Vice President of Data Platform to lead the strategy, architecture, and execution of our enterprise-wide data infrastructure. As a data-first media company operating at petabyte scale, data is the connective tissue across our editorial, product, advertising, and audience development functions. This is a defining leadership role for someone who can operate at the intersection of deep technical excellence and enterprise strategy — architecting the systems that power how we understand our content, our audiences, and our business. The SVP of Data Platform will own the full data stack: from ingestion pipelines and data warehousing to real-time streaming, ML/AI platform capabilities, and the governance frameworks that ensure our data is trusted, accessible, and compliant. This position is hybrid in-office, with the ability to work remotely for up to 2 days per week. People Inc. is a leading digital media company reaching millions of consumers across a diverse portfolio of iconic brands. Our content informs, entertains, and shapes culture and increasingly, data, at multi-petabyte scale, is the engine behind how we create, distribute, and monetize that content. Operating in a real-time, multi-platform ecosystem, we rely on data to understand audience behavior, optimize content performance, power advertising solutions, and unlock new revenue streams. The SVP of Data Platform will build and lead a high-performing team, partnering closely with Engineering, Data Science, Ad Tech, Research, Product, Editorial, Revenue, Business, and Marketing leadership, serving as the company's foremost authority on all things data.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related quantitative field is required
15+ years of progressive experience in data engineering, data platform architecture, or analytics infrastructure.
At least 5 years of experience in a senior leadership role.
Proven track record designing and scaling petabyte-level data platforms in a high-traffic, data-intensive industry (preferably media, e-commerce, fintech, or similar).
Deep expertise across the modern data stack: data lakes/lakehouses, cloud data warehouses (Snowflake, BigQuery, Redshift), stream processing (Kafka, Flink, Dataflow), and orchestration (Airflow, Dagster).
Strong command of cloud platforms (GCP, AWS) and experience managing large-scale distributed systems at production quality.
Experience establishing data governance programs, metadata management, and data quality frameworks at enterprise scale.
Strategic mindset: comfortable defining a 3-year roadmap and equally comfortable making pragmatic architectural trade-offs in the near term.
Exceptional communication and executive presence—ability to speak fluently with engineers and senior management alike.

Nice To Haves

A Master's or PhD is a plus.
Experience in digital media, content platforms, or advertising technology is strongly preferred.

Responsibilities

Define and execute a multi-year data platform strategy, aligned to company-wide goals across content, audience, advertising, and product.
Serve as the executive voice of data — advising the CTO and C-suite on data architecture decisions, investments, and organizational structure, influencing how the company measures success.
Translate complex data capabilities into clear business value for non-technical stakeholders.
Establish a data-first culture across the organization, advocating for data literacy, democratization, and responsible use.
Lead exploration and strategy around the foundational data products required to support use in both internal and external AI applications
Architect and oversee a petabyte-scale data platform spanning batch and real-time ingestion, transformation, storage, and serving layers.
Drive the evolution of our data warehouse/lakehouse strategy (e.g., BigQuery, Snowflake, DBT) and ensure the platform is built for performance, reliability, and cost efficiency at scale.
Lead adoption of emerging data and AI technologies.
Ensure the platform is designed for both technical and self-serve users, enabling analysts, data scientists, and product, and all other teams which use data to move quickly and independently.
Own data pipeline architecture across streaming (e.g., Kafka, Pub/Sub) and batch frameworks, ensuring low-latency data delivery at scale.
Establish enterprise data governance standards including ownership, lineage, cataloging, and data quality SLAs.
Define and enforce data privacy and compliance frameworks (GDPR, CCPA, and evolving state regulations), working in close partnership with Privacy, Legal, and Security.
Build a trusted data foundation — ensuring every downstream team operates from a single, authoritative source of truth.
Lead, mentor, and grow a high-performing data organization including Data Engineering, Platform Engineering, and Analytics Engineering functions.
Recruit and develop senior technical talent; build a team culture defined by ownership, craft, and continuous learning.
Partner with Engineering, Product, and Data Science leadership to align roadmaps and ensure data capabilities are tightly coupled to company priorities.
Define team structures, operating models, and performance metrics that scale with business growth.
Drive the company's data infrastructure readiness for generative AI and ML applications — including model training pipelines, embedding stores, and experiment tracking.
Partner with Product and Editorial teams to identify opportunities where advanced analytics and AI can create measurable impact.