Lead Analytics Engineer, Data Platforms in London

Lead Analytics Engineer, Data Platforms in London

London Full-Time 60750 - 74250 £ / year (est.) Home office (partial)
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At a Glance

  • Tasks: Lead the design of scalable data systems and align strategies with business goals.
  • Company: Kaluza, a forward-thinking company focused on cleaner energy solutions.
  • Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
  • Other info: Join a dynamic team in London, Bristol, or Edinburgh with a focus on sustainability.
  • Why this job: Make a real impact in the energy sector while leading innovative data practices.
  • Qualifications: Strong SQL/dbt skills and experience in stakeholder management.

The predicted salary is between 60750 - 74250 £ per year.

Kaluza is seeking an Analytics Engineering Lead to set the gold standard for our data practices.

You will architect scalable data systems, drive modelling strategy, and align technical work with business goals across the Data team.

Based in London, Bristol or Edinburgh, this hybrid role requires strong stakeholder management and deep knowledge of SQL/dbt, CI/CD, and data governance to deliver impactful solutions that power a cleaner energy future.

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Lead Analytics Engineer, Data Platforms in London employer: Kaluza

Kaluza is an exceptional employer, offering a dynamic work environment in London that fosters innovation and collaboration within the energy sector. With a strong commitment to employee growth, Kaluza provides a range of benefits including a personal learning budget, flexible holiday options, and a progressive leave policy, ensuring that team members can thrive both personally and professionally. The company's focus on diversity and merit-based talent recognition makes it an attractive place for those seeking meaningful and rewarding careers in business development.

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Contact Details:

Kaluza Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Lead Analytics Engineer, Data Platforms in London

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 Kaluza!

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 Lead Analytics Engineer, Data Platforms at Kaluza.

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 Kaluza.

Apply Directly through Our Website

When you find a suitable opening like Lead Analytics Engineer, Data Platforms at Kaluza, 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 Lead Analytics Engineer, Data Platforms in London

Python
Communication Skills
Data Engineering
SQL
ETL/ELT Processes
Problem-Solving Skills
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 Kaluza, 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 Kaluza. 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 Kaluza

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 Kaluza!

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.