At a Glance
- Tasks: Solve complex data problems and develop impactful analytics for business operations.
- Company: Join Dotmatics, a leading digital science platform transforming scientific research.
- Benefits: Remote-friendly environment, competitive salary, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on integrity and empowering team members.
- Why this job: Make a real impact in scientific innovation and help shape the future of research.
- Qualifications: 7+ years in analytics engineering, strong SQL skills, and experience with modern analytics tools.
The predicted salary is between 60000 - 80000 € per year.
At Dotmatics, we believe science, data, and decision-making must be deeply intertwined for innovation to thrive. Our global team of more than 800 colleagues are dedicated to supporting our customers in over 180 countries. Together, with our scientific community of users, we accelerate scientific innovation in order to make the world a healthier, cleaner, and safer place to live. You’ll join a collaborative, global team pushing the boundaries of scientific innovation. Your ideas and efforts will have a tangible impact, accelerating scientific progress and discovery. We offer a dynamic, remote-friendly environment that fosters high integrity and collaboration, empowering you to excel.
We're looking for a Senior Data Analytics Engineer who thrives in a fast-moving environment and enjoys solving complex data problems across a broad range of business functions. You bring hands-on experience building and maintaining data models, and you're equally comfortable diving into ad hoc analysis as you are designing scalable, automated reporting solutions. You have strong SQL skills and experience with modern analytics tooling — dbt, Looker, Snowflake, or similar — and you know how to translate raw data into insights that non-technical stakeholders can actually use. You're proactive about data quality, care about documentation and best practices, and can manage your own workload across multiple competing priorities. Experience working across the full analytics pipeline — from lead generation through to revenue — is a strong plus, as is a track record of enabling self-service analytics within a business.
In this role you will get to:
- Develop high-impact reporting and analytics providing visibility into business operations, lead efforts to scale reporting through automation.
- Partner with internal stakeholders (Sales, Product, Marketing, Finance, etc.) to increase the understanding, visibility and use of business metrics, encourage self-service analytics.
- Perform ad hoc data exploration and analysis to answer immediate business needs.
- Identify opportunities to improve data quality, performance, and observability.
- Help develop standards for analytics engineering best practices.
We are looking for people who have at least 7 years of experience in analytics engineering, data engineering or analytics-heavy roles. You will have a Bachelor’s degree in a technical or quantitative field (Computer Science, Engineering, Math, Economics, bioinformatics etc.), or equivalent experience.
The key skills we are looking for:
- Advanced SQL — strong understanding of CTEs, window functions, query optimization.
- Experience working directly with business teams to align on KPIs, define requirements, and deliver impactful insights.
- Comfortable writing and maintaining data dictionaries, dbt Docs, or internal wikis.
- Ability to design and build BI assets that are explainable and sustainable whether they are excel spreadsheets, BI dashboards or powerpoint presentations.
You may also have:
- Experience with Python and shell scripting is a plus.
- Familiarity with version control systems (e.g., Git) and CI/CD workflows for data engineering projects.
- Experience working with DBT/DBT core.
- Experience with multiple BI tools (e.g., Looker, Mode, Tableau).
- Curious, self-driven, analytical and excited to play with data.
- Foundation in data quality, testing, and documentation.
Research shows us the confidence gap and imposter syndrome can get in the way of meeting outstanding candidates, so please don’t hesitate to apply — we’d love to hear from you.
Dotmatics is an equal opportunity employer. We are a welcoming place for everyone, and we do our best to make sure all people feel supported and connected at work.
Senior Data Analytics Engineer employer: Dotmatics
At Dotmatics, we pride ourselves on being an exceptional employer, offering a dynamic and remote-friendly work environment that fosters collaboration and innovation. Our commitment to employee growth is evident through our supportive culture, where your ideas can lead to tangible impacts in the scientific community. Join us and be part of a global team dedicated to accelerating scientific progress while enjoying the benefits of a company built by scientists, for scientists.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Analytics Engineer
✨Tip Number 1
Network like a pro! Reach out to current employees at Dotmatics on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for your application process. Personal connections can make a huge difference!
✨Tip Number 2
Prepare for the interview by brushing up on your SQL skills and analytics tools. Be ready to showcase how you've tackled complex data problems in the past. We want to see your thought process and how you can translate data into actionable insights.
✨Tip Number 3
Don’t just focus on your technical skills; show us your soft skills too! Communication is key, especially when working with non-technical stakeholders. Practice explaining your past projects in simple terms to demonstrate your ability to bridge the gap.
✨Tip Number 4
Finally, apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at Dotmatics. Let’s shape the future of science together!
We think you need these skills to ace Senior Data Analytics Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Senior Data Analytics Engineer role. Highlight your SQL expertise and any experience with analytics tools like dbt or Looker. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to show us your personality and passion for data analytics. Share specific examples of how you've solved complex data problems in the past and how you align with our vision at Dotmatics. Let your enthusiasm shine through!
Showcase Your Projects:If you've worked on relevant projects, whether in a professional setting or as personal endeavours, include them in your application. We love seeing real-world applications of your skills, especially those that demonstrate your ability to drive insights from data.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're serious about joining our team at Dotmatics!
How to prepare for a job interview at Dotmatics
✨Know Your Data Tools Inside Out
Make sure you’re well-versed in the analytics tools mentioned in the job description, like SQL, dbt, and Looker. Prepare to discuss specific projects where you've used these tools to solve complex data problems, as this will show your hands-on experience.
✨Understand the Business Context
Familiarise yourself with how data impacts different business functions such as Sales, Product, and Marketing. Be ready to share examples of how your insights have driven decision-making in these areas, demonstrating your ability to translate raw data into actionable insights.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles and how you approached solving them. Highlight your proactive attitude towards data quality and your experience in developing standards for analytics engineering best practices.
✨Communicate Clearly with Non-Technical Stakeholders
Practice explaining complex data concepts in simple terms. You might be asked to present a past project or analysis, so ensure you can articulate your findings in a way that’s accessible to those without a technical background.