Senior Staff Data Engineer

Who are we? 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. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? 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. Everything is provisioned as code in Terraform, and governance is part of the design: 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. The roadmap is set with big bets in flight: scaling that framework across the company, standing up an internal analytics agent with a real eval harness, and consolidating institutional knowledge into a self-correcting and self-maintaining system that keeps context centralized, current, and documented, rather than dispersed and in people’s heads. Responsibilities As the head of the Data Foundation pillar, you will: Build and lead the team. 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. Set the roadmap from company strategy. 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. You’ll make the trade-offs and carry the cross-functional impact that comes with them. 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. All of it provisioned as code in Terraform. 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. Operate for reliability. 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. Lead the agentic analytics program. 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. Set the standard for analytics across the company. The medallion framework, naming conventions, and testing patterns your team maintains become the way other teams ship analytics data. You’ll onboard new domains onto it and keep the docs accurate to how the systems actually work. Partner across the org. 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. Qualifications You may be a good fit, if you have: 10+ years of experience across data engineering, data infrastructure, or analytics platform