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 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

• Comfort operating in ambiguity, leading a multi‑workstream roadmap, and working directly with executives on priorities

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 benefits, including a separate budget for mental health.

• RRSP matching, 401K, Pension Scheme.

• 100% Parental Leave top‑up for up to 6 months, for either parent.

• Annual enrichment benefits:

  Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.

  Education & learning stipend for conferences, courses, and coaching.

• 6 weeks of paid vacation (30 working days!)

• Budget for traveling to other offices if you are remote, plus an annual company offsite.

How and Where We Work:

• Cohere is remote‑friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon.

• For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.

• For those not near an office: a co‑working benefit so you can work alongside others in your city.

• Everyone receives a $500 home office stipend to set up your workspace properly.

We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.

We may use AI‑enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.