Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. We are a global technology company headquartered in Toronto with key offices in London, Recent York City, San Francisco, Montreal, Paris, Berlin and Seoul. We’re building the foundational infrastructure that will define how the world thinks about and deploys AI, and we want the sharpest, most driven, and most curious people to help us do it. As the head of the Data Foundation pillar of our Analytics and Data Insights team, you’ll lead the team that makes Cohere’s data trustworthy, governed, and available: the warehouse every analytics surface runs on, the pipelines that keep it fresh, the access model that keeps it secure, and the agentic analytics layer that lets anyone at Cohere (and their agents) ask questions of governed data directly. Your team owns the full data lifecycle. You’ll run ingestion into BigQuery from internal and external data sources, orchestrate it in Airflow and Cloud Run, model the data warehouse in dbt, and serve the results through Looker, export APIs, and customer-facing analytics surfaces. VPC service controls, IAM patterns,



and documented access paths that let people and agents move fast without putting data at risk. This role is both highly visible and highly impactful, and you’ll inherit a solid structure on which to build the rest of the stack: a Terraform-provisioned warehouse framework, dbt modeling conventions, an alerting and incident-response practice, and documentation patterns to keep everything discoverable. Hire and grow a team of data and infrastructure engineers; set the technical bar; develop strong talent and raise team performance, building leadership capability beneath you. Own and sequence the Data Foundation roadmap: the warehouse buildout, the agentic analytics program, data governance, and the platform work that unblocks our other pillars. Own the data platform end to end. Ingestion (managed connectors and an open‑source connector framework), orchestration on Airflow and Cloud Run, warehouse modeling in BigQuery with dbt (raw, core, mart layers), and serving through Looker, export APIs, and semantic layers. Own data governance. Define how humans, service accounts, and agents get access to governed data: VPC service controls, IAM patterns, documented onboarding paths, audit logs, and sensitive requests (including legal holds) handled to deadline. Run the alerting and anomaly‑detection framework, own data freshness SLAs, lead incident response and postmortems, and turn every incident into a durable standard so the same class of failure doesn’t recur.



Wire the semantic layer into an internal analytics agent, build the eval harness that measures its accuracy, and make the eval‑driven call on where AI earns its way into production. Apply the same discipline to automating data engineering work itself. The medallion framework, naming conventions, and testing patterns your team maintains become the way other teams ship analytics data. Work with the North product and engineering teams on the analytics surfaces that ship in the product (including a customer‑facing analytics export), with Business Insights on marts for the business, with Security on data governance, and with Legal on compliance‑critical data requests. 10+ years of experience across data engineering, data infrastructure, or analytics platform work, with a strong management track record leading data platform or infrastructure teams Serious hands‑on history in data engineering or infrastructure before management. Production experience with cloud data warehouses (BigQuery, Snowflake, or similar), workflow orchestration (Airflow or similar), and infrastructure as code (Terraform or similar) Depth in data modeling: you think in fact and dimension tables, raw/core/mart separation, and semantic layers, and you have opinions on where tests and contracts belong Exposure to data security and governance: IAM, access management, VPC service controls or equivalent, audit and compliance workflows Genuine excitement about AI: you follow the research, have opinions, and are energized by building the data foundation an AI company (and its AI agents) runs on Full‑Time Employees At Cohere Enjoy These Perks A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. Full health and dental b

📌 Data Modellers (Canada)
🏢 Cohere
📍 Canada