Director, Data Engineering – AI & Data Platforms

• Own the strategy architecture and roadmap for the companys data engineering and analytics platform.

• Design and oversee scalable secure and cost-effective data architectures and pipelines supporting analytics reporting machine learning and AI applications.

• Establish engineering standards for data modeling pipeline development testing deployment observability documentation and operational excellence.

• Lead the development and optimization of batch and near-real-time data pipelines using SQL and Python.

• Oversee data modeling and warehouse architecture in Snowflake ensuring scalability performance reliability and efficient use of resources.

• Drive the evolution of our cloud-based data infrastructure using AWS including S3 EC2 Lambda and related services.

• Establish robust frameworks for data quality testing monitoring lineage observability and alerting.

• Evaluate and introduce technologies that improve the scalability reliability and efficiency of the data platform.

• Balance hands-on technical contribution with architectural oversight and engineering leadership.

• Lead the data engineering strategy supporting machine learning generative AI and AI-powered applications.

• Partner with data scientists engineers analysts and business leaders to identify and prioritize high-value AI opportunities.

• Design and oversee data pipelines supporting model training feature engineering inference evaluation and monitoring.

• Develop the data foundations required for LLM and generative AI applications including data preparation embeddings vector data retrieval pipelines and RAG architectures where appropriate.

• Establish processes for AI data quality model evaluation experimentation and performance monitoring.

• Identify opportunities to use AI to improve internal workflows analytics data operations content-related processes and engineering productivity.

• Evaluate emerging AI technologies and determine where they can provide practical business value.

• Establish responsible and scalable approaches to incorporating AI into the companys data and technology ecosystem.

• Partner with leadership to develop an AI roadmap aligned with business priorities and measurable outcomes.

• Provide technical leadership and mentorship to data engineers and other technical contributors.

• Establish engineering best practices for code quality version control CI/CD testing documentation security and operational reliability.

• Define technical objectives priorities and development standards for the data engineering function.

• Participate in hiring onboarding coaching performance development and career growth for data engineering team members.

• Build a culture of technical ownership experimentation continuous improvement and knowledge sharing.

• Determine when to build buy or integrate third-party technologies and services.

• Promote reusable frameworks tooling and engineering practices that improve team productivity.

• Partner with analysts and business stakeholders to ensure the data platform supports reliable and accessible business intelligence and analytics.

• Improve data accessibility discoverability documentation and usability across the organization.

• Work with stakeholders to translate business requirements into scalable technical solutions.

• Support data experimentation and statistical analysis by ensuring analysts and data scientists have high-quality appropriately structured datasets.

• Help establish data definitions governance practices and standards that improve trust in company data.

• Communicate complex technical concepts and architectural decisions clearly to both technical and non-technical audiences.

• Own the reliability scalability and cost management of the organizations cloud-based data infrastructure.

• Oversee deployment and management of data applications and services using AWS.

• Establish appropriate practices for infrastructure automation CI/CD security access controls and operational monitoring.

• Identify opportunities to optimize cloud costs and platform performance.

• Develop disaster recovery resiliency and operational processes appropriate for the companys data infrastructure.

• Work closely with engineering and technology leadership on broader cloud infrastructure initiatives.

• Serve as a strategic technical partner to executive leadership product engineering analytics and business teams.

• Lead cross-functional initiatives involving data AI analytics automation and technology modernization.

• Translate technical capabilities and limitations into clear business implications and recommendations.

• Establish priorities across competing data and AI initiatives based on business value technical feasibility and available resources.

• Represent the data engineering function in broader technology and organizational planning.

• 8 years of experience in data engineering software engineering analytics engineering or a related technical discipline.
• 3 years of experience leading data engineering teams technical initiatives or data platform architecture.
• 5 years of experience with SQL including complex data exploration optimization and relational/data warehouse modeling.
• 5 years of experience with Python for data engineering automation application development or machine learning workflows.
• 3 years of experience with Snowflake including data modeling architecture performance optimization and large-scale data processing.
• 3 years of experience with AWS including services such as S3 EC2 Lambda and related cloud technologies.
• 3 years of experience with Apache Airflow or comparable orchestration technologies such as Dagster or Prefect.
• Demonstrated experience designing and implementing scalable data platforms and production-grade data pipelines.
• Strong experience with data quality testing observability monitoring and operational reliability.
• Demonstrated experience supporting machine learning and/or AI initiatives through data engineering feature engineering model data pipelines or AI infrastructure.
• Strong understanding of machine learning concepts statistical analysis experimentation and data science workflows.
• Working knowledge of generative AI LLMs embeddings vector search RAG and AI application architectures.
• Strong analytical and critical-thinking skills with the ability to solve complex technical and business problems.
• Demonstrated ability to communicate technical concepts clearly to technical and non-technical audiences.
• Experience working effectively with executives stakeholders engineers analysts and data scientists.
• Must be willing to work in our NY or LA office.

Preferred Qualifications:

• Experience leading AI transformation or AI platform initiatives.
• Experience designing production systems for LLM or generative AI applications.
• Experience with vector databases embeddings RAG architectures model evaluation and AI observability.
• Experience implementing AI-powered automation within data or business workflows.
• Experience with infrastructure-as-code technologies such as Terraform.
• Experience with CI/CD Docker Kubernetes or other modern DevOps practices.
• Experience with distributed data processing technologies such as Spark.
• Experience with data governance metadata management lineage security and privacy.
• Experience working in a digital media advertising publishing entertainment or consumer technology environment.
• Experience managing data platforms in a high-growth or resource-constrained organization.
• Experience evaluating third-party data analytics and AI platforms and negotiating build-versus-buy decisions.