Agentic analytics is far from a solved problem, and correctness is the hard part. A query that runs can still answer the wrong question. Agents go wrong when a business term maps to more than one table, when data goes stale, and when the right context never reaches the model. The worst failures are silent and plausible: a join that double-counts, a wrong denominator, missing data read as zero. Getting this right takes governed definitions, context an agent can use, and an evaluation bar that decides what ships.
You'll build on work already in motion: an internal analytics agent in pilot on our BigQuery warehouse, which is fed by tested ingestion pipelines and modeled in dbt. In your first year, you'll take it to production for teams across Cohere, one domain at a time, and make it replicable: each new domain stands up with configuration, a fresh evaluation set, and very little new code.
This is a hands-on staff engineering role reporting to our Senior Director of Data Analytics. You'll write production code and evaluation sets yourself while managing a small founding team that grows as the system proves itself. You may have held the title software engineer, AI engineer, or data scientist; what matters is that you've built a system like this and run it in production.
RESPONSIBILITIES
As the lead of Agentic Analytics, you will
Own the system end to end. Set the architecture for how a business question becomes a governed query and a verifiable answer: interpreting the question, retrieving the right context, applying approved definitions and joins, enforcing the asking user's permissions, and attaching the evidence (source, query, freshness) to every answer.
Build and run it in production. Design the core services and write much of the first version yourself: agent-facing data tools (MCP or equivalent), context retrieval, agent skills for analytics, and the APIs that serve answers. Hold them to production standards for reliability, latency, cost, security, and observability. Put it where people already work: Slack, North, IDEs, and other agents.
Hold every release to the evaluation bar. Build and run the evaluation harness, and define what correct means in each domain: ground-truth question sets, including held-out cases and questions the agent should clarify or decline; result-level scoring, correct-or-decline rates, coverage, and error severity; and regression tests on every change to context, skills, or models. Nothing ships below the bar, and the call on when a domain is ready for production is yours.
Engineer the context layer. Turn metric definitions, approved joins, documentation generated from the code that builds each table, a business glossary, and worked examples into context the agent can use. Every correction becomes a reviewed definition and a regression test.
Roll it out across Cohere. Bring domains such as marketing, revenue, finance, and product onto the system one at a time, each with named owners, certified definitions, and its own evaluation set. Measure adoption, accuracy, turnaround, and correction effort against how those teams work today.
Make it replicable. Package the tools, skills, context templates, and evaluations, and document them well enough that other teams at Cohere can run the system without you.
Hire and lead the team. Bring on the team's first engineers, set the technical bar, review the work, and develop the people doing it.
Partner across Cohere. Work with our data platform team on the warehouse and semantic models, our model evaluation team on evaluation methodology, our security team on access, and each domain's owners on definitions and rollout.
QUALIFICATIONS
You may be a good fit, if you have
10+ years building and operating production analytics or software systems, including technical leadership of a team or of systems that span teams
Experience shipping LLM agents to production: tool use, MCP or similar, retrieval and context engineering, guardrails, and observability, with firm opinions on where they fail
Hands-on experience building natural-language or agentic analytics (text-to-SQL, analytics agents, question answering over a semantic layer) that real users relied on, and evidence that it was right
Depth in evaluating LLM systems: golden sets, result-level comparison, abstention metrics, error taxonomies, and regression testing in CI, plus the judgment to tell a good demo from a system you'd put in front of a CFO
Strong Python and SQL, and experience designing and running production services and APIs on a major cloud (Google Cloud is a plus)
Fluency with data: cloud warehouses (BigQuery, Snowflake, or similar), dbt, and semantic layers (MetricFlow, LookML, Cube, or similar). You think in grain, metric definitions, and approved joins, and you can tell when a number is wrong before it reaches a decision
Experience building identity-aware access for agents, where the agent acts with the asking user's permissions and every query leaves an audit trail
Experience hiring and developing engineers, with a leadership style grounded in empathy as much as in data
Genuine excitement about AI: you follow the research, have opinions, and enjoy being in the weeds
Comfort operating in ambiguity, leading through partners you don't manage, and writing design docs and eval reports that settle decisions across teams
If some of the above doesn't line up perfectly with your experience, we still encourage you to apply!
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.
If any of the above doesn’t line up exactly with your experience, we still encourage you to apply.
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 https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, 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.
Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.
