Analytics Engineer

Hey! We're team Granola If you haven't already, you should check out what we're building, and why you should work here.

We’re looking for a Analytics Engineer who loves turning raw data into powerful insights and predictive models. You’ll work closely with product, engineering, and ops to help shape our roadmap — from hypotheses, to experiments, to scalable models in production.

You'll have a big impact on how we unlock value from our data, and scale Granola’s analytics and intelligence capability as we 100× in users.

In this role, you will:

• Own Granola's data infrastructure

• Maintain and improve pipelines between our data warehouse and various data sources, including our production database, Amplitude/Posthog, and other SaaS tools we use, and build new pipelines.

• Build improvements to our data transformation process using SQL and dbt, optimising for performance and ease of querying.

• Make architecture decisions regarding data infrastructure.

• Define metrics and dashboards to track business health and model performance.

• Continuously evaluate and iterate on models in production — improving, retraining, and optimising.

• Mentor others in data thinking, share knowledge, and help elevate our analytical culture.

Your background looks something like:

• Several years doing applied data science / machine learning in a product environment (consumer SaaS, fintech, marketplace or similar).

• Strong foundation in statistics, experimentation, causal inference, and common ML techniques (regression, tree ensembles, clustering, time series).

• Hands-on experience with ML frameworks and tools (e.g. Python, scikit-learn, PyTorch or TensorFlow, MLflow or equivalent).

• Experience deploying models (e.g. via REST endpoints, batch jobs, feature store) and monitoring them.

• Comfortable working with data pipelines, SQL, data engineering, ETL, handling large datasets.

• Strong communication skills — able to explain technical insights to non-technical stakeholders.

• Bonus: experience with reinforcement learning, LLMs or generative AI, real-time streaming, or causal modelling.

As a person, you…

• Are first and foremost a builder — you want to go from zero to one, and ship things that matter.

• Thrive in working in person from our London office (most of the time).

• Love the challenges (and messiness) of a growing startup environment — you either have startup experience or are drawn to that kind of fast feedback loop.

• Value working with people who are kind, ambitious, and pragmatic.

• Are excited about AI/ML, and driven by turning data into impact (direct experience not strictly required in every domain).

• Enjoy ambiguity, shifting priorities, and iterating quickly.

About the opportunity
We are living in the most exciting time for tool builders since Engelbart's demo in 1968. We want to assemble the best crew to build this future together, here in London.

Our compensation philosophy is to pay slightly above market on salary and above market on equity.We do our best work in person, and so our team spends time together five days per week in our new, bright, and spacious office at Old Street.

We are happy to offer relocation assistance to candidates who'll be moving to London to join us.Lastly, we think amazing talent comes from all kinds of life journeys and experiences.

If what is written above speaks to you, whether you look like a fit on paper or not, please reach out.