> Markdown version of [/jobs/ext/2291453-data-analyst](https://www.wearedevelopers.com/jobs/ext/2291453-data-analyst). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Analyst - **Company:** femtasy - **Location:** Berlin, Germany - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Raw Data, SQL Databases, Tableau (Software) - **Published:** August 29, 2026 - **Apply:** https://www.adzuna.de/details/5857315270 ## About the Role * 3+ years of experience in a data analyst, business analyst, or similar analytics role: you can work independently on well-scoped problems and know when to ask for help on ambiguous ones. * Proven stakeholder-facing judgment. You've sat in front of a non-technical stakeholder with a vague ask, asked the right questions, and resisted both over-engineering the answer and under-scoping it. * You frame problems clearly, communicate directly, and challenge assumptions. * Solid SQL, dbt, and hands-on comfort with a BI tool: you build and maintain your models yourself, from raw data to fact table to dashboard. Our Analytics Engineer supports you on infrastructure, but day-to-day dbt work is yours to own. * Comfortable moving across domains. You won't own one fixed area - you will support Marketing and other stakeholders when needed. * Opinionated but coachable. You're willing to challenge »it's always been like that«, and open to direct feedback. * Fluent in English. A strong plus, not a must * You've worked with Marketing before (at least one real marketing project) * You've experienced a startup environment * You already use AI tools in your day-to-day analysis ## Description Today, all data requests from Marketing (our principal stakeholder), Product, and Finance funnel through our Head of Data, who scopes every ask before an external agency executes it. That doesn't scale. You'll be the first internal hire to change that: sitting directly with stakeholders, turning vague requests into well-defined problems, and delivering a good-enough solution fast. You'll report to the Head of Data and work alongside an Analytics Engineer who owns the heavier dbt/pipeline work. Six months in, this is what your impact looks like: you run scoping conversations with Marketing especially, but also Product and Finance independently - no pre-framing needed from your Head of Data. You own the fact tables and models behind your recurring reporting. You've proactively fixed or flagged at least one foundational data issue - because you saw it, not because someone asked. Stakeholders treat you as a go-to resource, not a ticket-taker. What you'll do * Own first-line scoping conversations with Marketing, Product, and Finance: ask the right clarifying questions, push back on vague or over-scoped requests, and land on a defined problem plus a good-enough solution. * Come up with your own solution after aligned with stakeholders, maximizing the impact and keeping the development time as short as possible. * Build and maintain the dashboards and reports (Tableau today, tool-agnostic long term) that Marketing, Product, and Finance can actually act on. * Build and manage data in dbt: create and maintain the fact tables and models that support your recurring reporting and analyses, with our Analytics Engineer supporting the underlying infrastructure. * Own the day-to-day reliability of what you build: catch issues, flag or fix them - so no one ever has to ask »which number is right«. * Question the status quo at the working level. If something's »always been done that way« and it's slowing a team down, we want you to say so and to work with your Head of Data on what's worth fixing. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner](https://www.wearedevelopers.com/videos/1308-data-science-ml-ai-in-the-oil-and-gas-industry-at-ndt-global-dr-katja-traumner) ## Related Articles - [Data Analyst Salary Germany](https://www.wearedevelopers.com/magazine/277-data-analyst-salary-germany) - [Data Analyst Salary Austria](https://www.wearedevelopers.com/magazine/275-data-analyst-salary-austria) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [The Biggest German Tech Companies](https://www.wearedevelopers.com/magazine/424-the-biggest-german-tech-companies) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)