Director, Data Engineering – AI & Data Platforms

Own the strategy, architecture, and roadmap for the company's 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 company's 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 organization's 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 company's 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.