At a Glance
- Tasks: Lead the design of data models and transform datasets for trusted insights.
- Company: Established UK organisation prioritising data and analytics with a people-first culture.
- Benefits: Flexible working hours, supportive environment, and a 35-hour work week.
- Other info: Join a dynamic team with opportunities for mentorship and technical leadership.
- Why this job: Make a real impact in a growing Lakehouse programme with visibility across the business.
- Qualifications: Experience as an Analytics Engineer, strong SQL and PySpark skills, and data modelling expertise.
The predicted salary is between 67000 - 67000 € per year.
This Analytics Engineer role stands out as a chance to play a key part in a large scale Lakehouse programme, sitting at the intersection of engineering, analytics and the wider business. You will take real ownership of curated data models, shape how data is structured and served across the organisation, and influence best practice as the analytics engineering capability continues to grow.
The Company
They are a large, well established UK organisation with a strong reputation for combining technical excellence with a people first culture. Data and analytics are a strategic priority, with ongoing investment into a modern cloud based data platform. Engineering teams are expanding as part of a broader transformation, creating genuine opportunities to have impact and influence.
The Role
You will join a growing analytics engineering team and play a critical role in the Lakehouse environment. Your responsibilities will include:
- Leading the design and delivery of curated, analytics ready data models within the Lakehouse
- Owning the transformation from enriched to curated datasets, enabling trusted reporting and insight
- Developing and maintaining robust SQL and PySpark transformation pipelines in Databricks
- Embedding data quality, testing, reliability and performance into the curated layer
- Working closely with data engineers, BI teams and business stakeholders to translate complex requirements
- Providing technical leadership, mentoring and setting modelling and engineering standards
- Contributing to CI/CD processes and wider engineering best practice across the data platform
Your Skills and Experience
- Strong commercial experience as an Analytics Engineer within a modern data platform
- Excellent data modelling capability, including dimensional and semantic modelling
- Advanced SQL skills and strong hands on experience with PySpark
- Experience working with Databricks and Lakehouse architectures
- A solid grounding in engineering best practices, testing and data quality
- Confidence mentoring others and taking ownership of technical decisions
- An engineering mindset applied to analytics, rather than an analyst focused role
What They Offer
- Flexible by choice working, supporting different schedules and work life balance
- A 35 hour working week within a supportive, inclusive engineering culture
- The opportunity to shape a critical data programme with real visibility across the business
How to Apply
If you are an Analytics Engineer looking to make an impact in a growing Lakehouse environment, apply now to find out more.
(197427) Analytics Engineer in London employer: LinkedIn
This company is an excellent employer, offering a supportive and inclusive engineering culture that prioritises work-life balance with flexible working options. Employees benefit from a 35-hour work week and the opportunity to shape a critical data programme, ensuring meaningful contributions are recognised and valued. With ongoing investment in modern cloud-based data platforms, there are ample opportunities for professional growth and influence within a well-established organisation known for its technical excellence.
StudySmarter Expert Advice🤫
We think this is how you could land (197427) Analytics Engineer in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those already working in analytics engineering. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data models and transformation pipelines. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your SQL and PySpark skills. Be ready to discuss your experience with Databricks and Lakehouse architectures, as these are key to landing that Analytics Engineer role.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace (197427) Analytics Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experience mentioned in the job description. Highlight your analytics engineering experience, especially with data modelling and SQL, to show us you're the right fit for the role.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about analytics engineering and how you can contribute to our Lakehouse programme. Be specific about your achievements and how they align with our goals.
Showcase Your Technical Skills:Don’t forget to mention your hands-on experience with PySpark and Databricks. We want to see how you've applied these skills in real-world scenarios, so include examples that demonstrate your expertise.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates during the process.
How to prepare for a job interview at LinkedIn
✨Know Your Data Models
Make sure you brush up on your data modelling skills, especially dimensional and semantic modelling. Be ready to discuss how you've designed and delivered curated data models in the past, as this will show your understanding of the role's requirements.
✨Showcase Your SQL and PySpark Skills
Prepare to demonstrate your advanced SQL skills and hands-on experience with PySpark. You might be asked to solve a problem or optimise a query, so practice common scenarios that could come up during the interview.
✨Understand the Lakehouse Architecture
Familiarise yourself with Lakehouse architectures and Databricks. Being able to articulate how these technologies fit into the broader data platform will impress the interviewers and show that you're aligned with their strategic priorities.
✨Emphasise Collaboration and Leadership
Since the role involves working closely with data engineers and BI teams, be prepared to discuss your experience in mentoring others and leading technical decisions. Share examples of how you've influenced best practices in previous roles to highlight your leadership capabilities.
