About the role
Culture Amp's benchmark dataset is one of the largest and most comprehensive collections of employee engagement data in the world. It's central to how our customers understand whether their engagement scores are strong or need attention, and it's a key reason companies choose Culture Amp.
We're looking for a Data Analyst to join our Data Products team and become the go-to expert on this dataset. You'll connect directly with our customer-facing teams (sales, customer success, and people science) to answer questions about our benchmarks, support deals, and help customers get the most from their data. This is a role with two sides: you'll spend time responding to requests and performing ad-hoc analyses, both for benchmarks and our other curated data models. But you'll also have the opportunity to reduce that reactive load over time by building better self-serve tools, improving documentation, and automating repetitive work. You'll work closely with the data scientists who build and maintain our benchmark pipelines. There's genuine opportunity here to grow — whether that's into more senior analytics work or picking up aspects of the data science side if that interests you.
What you'll do
- Field benchmark requests from customer-facing teams, translating business questions into data answers.
- Investigate coverage, methodology, and metadata questions across our benchmark datasets.
- Support custom benchmark creation for strategic deals.
- Build and improve Looker dashboards and self-serve tooling so teams can answer more questions themselves.
- Document tribal knowledge and make our benchmark data more accessible across the company.
- Partner with data scientists to understand and maintain benchmark pipelines.
- Support broader analytics needs: dbt modelling, dashboards, and analysis for other data science models and projects.
What we're looking for
- Around 2–4 years of experience in analytics, data analysis, or a similar role
- Strong SQL skills — this is your primary tool
- Experience with BI tools (Looker is ideal, but we're happy if you've worked with others and are keen to learn)
- Comfortable working with Python/Jupyter notebooks — you don't need to be a software engineer, but you should be able to run and understand existing scripts
- Experience with dbt or similar transformation tooling is a plus
- Clear communication skills — you'll be explaining data concepts to people who don't have a data background
- Curiosity about the "why" behind the numbers, not just the "what" The most important thing is that you're genuinely interested in understanding this dataset deeply. The technical skills matter, but domain knowledge is what will make you effective.
