The opportunity
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.
What you'll do
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.
What they're looking for
- + 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. You can still read a Terraform plan, a dbt DAG, or an Airflow scheduler log and ask the question that matters
- 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
- A record of setting a team’s roadmap from company or function strategy and: delivering outcomes that span teams, with the cross-functional partnerships to match
- Operational excellence you can point to: alerting you trust, SLAs you meet, incidents you run to ground and write up so they teach
- 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