Senior AI Data Engineer, Copilot

Job Description At Zendesk, our focus is helping our customers build great relationships with their customers. Founded by three Danish entrepreneurs, Zendesk has experienced remarkable success and growth while maintaining a fun, positive, and down-to-earth culture. We are looking for a Senior AI Data Engineer to join our AI Copilot organisation. AI Copilot is a multi-million ARR product that puts AI directly into the hands of customer service agents and administrators — powering real-time suggestions, automated actions, and proactive insights. You will build and scale the data infrastructure that underpins these AI/ML features, ensuring our solutions are accurate, cost-efficient, and continuously improving as we grow. We ship to learn: our philosophy is to deliver early, deliver often, and iterate based on real-world customer feedback. What you'll be doing Design, build, and maintain scalable ELT pipelines that power AI Copilot's ML-driven features. Build data infrastructure that enables real-time, contextually relevant AI capabilities and continuous learning at scale. Collaborate closely with ML Scientists and Engineers to support model experimentation, evaluation, and production deployment for LLM-powered features. Build and optimise SQL-based data models using dbt to enable AI insights, analytics, and self-service ROI measurement. Implement cost optimisation strategies for large-scale AI data pipelines — driving down operating costs while maintaining quality and reliability. Create data infrastructure to support ML model monitoring, evaluation, and key product metrics. Contribute to the team's technical vision for building a self-improving AI product that gets better as it scales. Work with feature teams, platform teams, and product to ensure reliable data foundations for AI capabilities. What you bring to the role Required 5 years of data engineering experience building, maintaining, and working with data pipelines and ELT processes in big data environments. Extensive experience with SQL, ideally in the context of data modelling and analysis. Hands-on production experience with dbt, and proven knowledge in modern and classic data modelling (Kimball, Inmon, etc.). Fluent in Python; exposure to Ruby is a plus. Experience with Snowflake or other cloud columnar databases (Amazon Redshift, Google BigQuery). Previous experience working with AI/ML products and understanding of ML workflows. Proven experience in performance testing, capacity planning, and cost optimisation for large-scale data pipelines and systems. Experience with Kafka, Docker, Kubernetes, and cloud platforms (AWS). Ability to work with uncertainty and the flexibility to pivot with changing priorities. Strong collaboration skills and a genuine desire to help the team succeed. Preferred Experience with Metaflow for ML pipeline orchestration. Experience with LLM evaluation and monitoring tooling. Experience with experiment tracking tools (e.g., MLflow). SnowPro Core certification or equivalent hands-on expertise. Hands-on production experience with Apache Spark (Spark SQL / PySpark). Familiarity with Tableau or Looker for BI and analytics visualisation. Experience building data infrastructure for product-led growth and self-service analytics. Tech Stack Our code is written in Python and Ruby Our servers live in AWS Our ML pipelines use Metaflow Our experiment tracking uses MLflow Our data is stored in S3, RDS MySQL, and Snowflake (with dbt for transformations) Our ELT stack includes dbt and Kafka Our services and models are deployed to Kubernetes using Docker Heavy usage of LLM technology from multiple providers BI tools include Tableau and Looker Infrastructure managed with Terraform and GitHub Actions #LI-AO1 Hybrid: In this role, our hybrid experience is designed at the team level to give you a rich onsite experience packed with connection, collaboration, learning, and celebration - while also giving you flexibility ... (truncated, view full listing at source)