Analytics Engineer

Responsibilities

• Design, own, and evolve core data models and the modelling architecture; including how they connect to pipelines, freshness, and operational reliability in the warehouse.
• Empower the organization to use and interpret data.
• Partner with business and technical stakeholders from problem framing to shipped artefacts: clarify requirements, suggest better approaches when a request would not scale, and drive or co-drive cross-team initiatives that improve how data is used.
• Collaborate closely with other data professionals; this includes giving actionable feedback in code reviews as well as actively supporting with data modeling
• Combine subject matter expertise and a high level business understanding to contribute to the direction and strategy for Mentimeter in the realm of data modeling, AI-Analysis, BI-tooling and data activation in GTM-Tooling.
• Develop, own and maintain data modeling standards and the data development experience
• Improve how we work at scale: thoughtful automation and AI-assisted engineering (e.g. coding agents and repeatable workflows) with human accountability for correctness and compliance.
• Maintain and grow how knowledge is surfaced, e.g in semantic models, a data catalog, or documentation
• We believe you have:
• Roughly 4-7 years of relevant hands-on experience as an Analytics Engineer, Data Analyst, Data Scientist, BI Analyst, or similar (or equivalent depth if your path is non-linear).
• Strong SQL and sound judgment in data modelling (grain, reusability, naming, and how metrics should behave).
• Experience partnering across departments, including leading or co-leading cross-functional work that shipped something others use.
• Strong business acumen with an understanding of sales, marketing, and product analytics.
• Ability to map ambiguous stakeholder needs to clear, maintainable models and artefacts.
• Experience with BI-tools and development of BI features
• Knowledge of software collaboration using git.
• Communication in English with both technical and non-technical audiences; interest in helping others grow their data skills.
• Experience working with GDPR-sensitive data and collaborating with legal experts on compliance assessments.
• It is beneficial if you have:
• Hands-on experience with dbt (or similar)
• Experience with a semantic or metrics layer
• Experience using AI-assisted coding or coding agents in a disciplined way (reviews, tests, documentation), with clear judgment about automation vs verification for production data work.
• Professional experience working in an agile environment
• Not required:
• Swedish is not required (daily work is carried out in English and the Mentimeter team currently boasts 50+ different nationalities).
• A cover letter is not required as part of your application
• 📍 Location: This is an onsite/hybrid role at Mentimeter HQ in Stockholm .
• Please note that we do not offer visa re

Benefits

Additional Information

Mentimeter is an engagement tool with a clear goal in mind. To turn presentations into conversations. Through real-time interactivity and clear visualizations, we get people to participate, engage and become more productive. Transforming all those passive meetings, airless classrooms and drawn out trainings into valuable and memorable moments. We truly believe that you achieve the best results by doing things together. And that successful leaders need to adopt a curious and collaborative mindset in order to get there. So with you at Mentimeter, you'll be a big part of the ambition to help over 1 billion people listen, learn and work better together. As an Analytics Engineer in the Analytics Foundation team, you help the whole company use data with confidence. You sit in a small team with real ownership: we shape how analytics engineering is done here, not only what gets built. You will make architectural decisions for data modelling and set the bar for quality, maintainability, and how we work. This is not a role where you only implement models from a backlog of "orders"; you partner with the business and with technical teams, surface trade-offs when needed, and help prioritise work that compounds. We spend most of our time on larger initiatives that improve the platform and how people use data, rather than reacting to a constant stream of ad-hoc questions. The team has a high degree of autonomy over its agenda, little firefighting, and we have invested for years in automating repetitive work so we can focus on impact. You will work hands-on with modern data tooling and with AI-assisted workflows, including coding agents and agent-style automation where they genuinely speed up design, review, and deliver, while staying responsible for correctness, governance, and clarity for stakeholders. You will develop and maintain core models, quality practices, and governance; support the warehouse and pipelines; and help teams get the most from BI and GTM tooling. Tools in our stack include Snowflake, dbt core, Hex, Prefect, Fivetran, and Hightouch (among others).

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