AI-Powered Analytics Engineer - Data Warehouse & Insights

AI-Powered Analytics Engineer - Data Warehouse & Insights

Full-Time 54000 - 66000 £ / year (est.) No working from home possible
Fresha

At a Glance

  • Tasks: Model and transform data to drive decision-making and enhance product features.
  • Company: Join Fresha, a dynamic company at the forefront of AI-powered analytics.
  • Benefits: Enjoy competitive pay, flexible work options, and opportunities for professional growth.
  • Other info: Collaborative environment with a focus on innovation and career advancement.
  • Why this job: Be part of a fast-paced team using cutting-edge AI tools to make an impact.
  • Qualifications: Experience in data modelling and a passion for analytics and AI technologies.

The predicted salary is between 54000 - 66000 £ per year.

Fresha is seeking an Analytics Engineer to model and transform data, powering decision-making and product features.

You will work with Product Managers, Engineers, Analysts and Commercial Leadership to build robust data models, tests, and documentation.

You will enhance data model performance, ensure data accessibility, and support analytics across commercial and product teams in a fast-paced environment.

A strong focus on AI tooling and modern data stack will be essential.

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AI-Powered Analytics Engineer - Data Warehouse & Insights employer: Fresha

Fresha is an exceptional employer that fosters a dynamic and innovative work culture, particularly for the Remote Business Development Manager role in Belfast. With a focus on employee growth and autonomy, Fresha offers a unique opportunity to make a significant impact in a fast-paced environment while enjoying the flexibility of remote work. The company prioritises collaboration and provides comprehensive support, ensuring that employees have the tools and resources needed to thrive in their roles.

Fresha

Contact Details:

Fresha Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land AI-Powered Analytics Engineer - Data Warehouse & Insights

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 Fresha!

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 AI-Powered Analytics Engineer - Data Warehouse & Insights at Fresha.

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 Fresha.

Apply Directly through Our Website

When you find a suitable opening like AI-Powered Analytics Engineer - Data Warehouse & Insights at Fresha, 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 AI-Powered Analytics Engineer - Data Warehouse & Insights

Python
Communication Skills
SQL
Problem-Solving Skills
Data Pipeline Development
Data Engineering
API Integration

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 Fresha, 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 Fresha. 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 Fresha

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 Fresha!

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.