Analytics Engineer: Build Trusted Data Models & Dashboards

Analytics Engineer: Build Trusted Data Models & Dashboards

Full-Time 35000 - 45000 £ / year (est.) No working from home possible
Global

At a Glance

  • Tasks: Transform raw data into business-ready datasets and metrics for confident use.
  • Company: Join a global team focused on data excellence and collaboration.
  • Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
  • Other info: Dynamic team environment with a focus on innovation and best practices.
  • Why this job: Make an impact by building trusted data models that drive business decisions.
  • Qualifications: Experience in data analytics and a passion for transforming data into insights.

The predicted salary is between 35000 - 45000 £ per year.

Global is seeking an Analytics Engineer to join the Data team.

You will transform raw data into curated, business-ready datasets and shared metrics for Analysts and other collaborators to use with confidence.

You’ll work with Data Engineering and Analytics teams to apply business logic to datasets, ensuring best practices and clear definitions for BI, Data Science and analytics needs.

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Analytics Engineer: Build Trusted Data Models & Dashboards employer: Global

Global is an exceptional employer that fosters a culture of innovation and collaboration, making it an ideal place for professionals looking to make a significant impact in the tech industry. With a strong emphasis on employee growth and development, you will have access to numerous opportunities to enhance your skills and advance your career while working alongside visionary leaders in a dynamic environment. Located at the forefront of technological advancement, Global offers unique advantages such as exposure to cutting-edge AI initiatives and a commitment to continuous improvement.

Global

Contact Details:

Global Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Analytics Engineer: Build Trusted Data Models & Dashboards

Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Global!

Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Analytics Engineer: Build Trusted Data Models & Dashboards at Global.

Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Global.

Apply Directly through Our Website

When you find a suitable opening like Analytics Engineer: Build Trusted Data Models & Dashboards at Global, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Analytics Engineer: Build Trusted Data Models & Dashboards

SQL
Python
Communication Skills
Problem-Solving Skills
Automation
Data Engineering
Data Pipeline Development

Some tips for your application 🫡

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at Global, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Global. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at Global

Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Global!

Prepare for Case Studies

Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.