Director, Platform Design & Intelligence

Co-design the platform architecture and roadmap with both leaders and drive implementation with the team. Deepen our structured data lake and build reliable pipelines, warehouse models, and reusable services with SQL, Python, Snowflake, and AWS.

Recommend what to build, buy, integrate, or retire. Implement agreed changes that remove unnecessary middleware, redundant licenses, and single-purpose tools.

Develop and implement standards with engineering colleagues for data quality, testing, lineage, observability, and deployment. Improve performance, cloud costs, resilience, and recovery together.

Work with data scientists and data engineers to understand our audience and help develop and implement segmentation across teams.

Design the signal capture that connects audience interactions to useful data. Make that data accessible and trustworthy for analytics, experimentation, content strategy, and product decisions.

Partner with content, product, and engineering teams on proprietary tools and new audience interactions, with a clear place in the broader platform.

Build the data foundations for machine learning and generative AI, including training and inference pipelines, embeddings, vector search, and retrieval-augmented generation where appropriate.

Test new models and tools, evaluate results, and recommend priorities to both leaders. Implement agreed AI and agentic applications with the team, including evaluation and monitoring.

Work closely with development, security, and applied AI colleagues to protect personally identifiable information and implement appropriate access controls. Make responsible design part of how the team builds and iterates.

Stay hands-on in implementation and architectural refinement while mentoring engineers. Support hiring and development, and help establish practices for code quality, version control, CI/CD, and operational reliability.

Help teams adopt the tools you build. Explain technical choices clearly, teach colleagues how systems work, and connect their use to content strategy and everyday decisions.

Translate business needs into implementation priorities with both leaders and cross-functional partners. Clarify responsibilities and tradeoffs, coordinate delivery, and measure results against agreed outcomes.

8+ years in data engineering, software engineering, analytics engineering, or a related discipline, including 3+ years leading teams, technical initiatives, or data platform architecture.

5+ years each with SQL and Python, including complex data exploration, optimization, warehouse modeling, and production data or automation workflows.

3+ years each with Snowflake; AWS services such as S3, EC2, and Lambda; and Apache Airflow or comparable orchestration tools such as Dagster or Prefect.

Experience building scalable production data platforms and pipelines, with strong data quality, testing, observability, and operational practices.

Experience supporting machine learning or AI through data engineering or infrastructure; an understanding of statistical analysis, experimentation, and data science workflows; and working knowledge of LLMs, embeddings, vector search, RAG, and AI application architecture.

Strong systems design and problem-solving skills. You can explain technical decisions to engineers and non-technical colleagues, work effectively with executives and cross-functional teams, and help others put new tools into practice.

Curiosity about our audience, sound judgment around security and data use, and a willingness to keep learning as models and tools change.

You must be willing to work on-site in our New York or Los Angeles office.

Leading AI transformation or platform initiatives; building production LLM applications and AI-powered automation; and evaluating models, retrieval systems, and AI performance.

Infrastructure as code such as Terraform; CI/CD, Docker, or Kubernetes; distributed processing such as Spark; and data governance, metadata, lineage, security, and privacy.

Digital media, advertising, publishing, entertainment, or consumer technology; high-growth or resource-constrained teams; and evaluating third-party platforms and build-versus-buy decisions.