At a Glance
- Tasks: Lead the Analytics Engineering function and design high-quality data models.
- Company: A large, innovative organisation investing in enterprise data platforms.
- Benefits: Competitive salary, hybrid work, and opportunities for professional growth.
- Other info: Collaborative team with a focus on modern engineering practices.
- Why this job: Join a pivotal Lakehouse programme and influence data governance across the organisation.
- Qualifications: Strong experience in analytics engineering, SQL, and PySpark.
The predicted salary is between 78000 - 78000 £ per year.
This is a rare opportunity to take a lead role in a growing Analytics Engineering function at a pivotal moment in its evolution. You will play a central part in a major Lakehouse programme, combining deep technical ownership with the chance to influence how data is modelled, governed and consumed across the organisation.
The Company
They are a large, complex organisation making continued investment in their enterprise data platform. Analytics sits at the heart of decision-making, with a clear mandate to expand engineering capability and mature the Lakehouse environment. The team is collaborative, technically ambitious and growing, with strong links across Engineering, BI and the wider business. This role sits within a well-established analytics function that is scaling its engineering remit.
The Role
- Take ownership of the curated or Gold layer within a Lakehouse architecture
- Design and deliver high-quality, analytics-ready data models that support trusted reporting
- Translate complex business requirements into robust SQL and PySpark transformation pipelines
- Shape how data is structured, modelled and served across multiple domains
- Embed data quality, testing, reliability and performance standards into pipelines
- Contribute to CI/CD and modern engineering best practices within the data platform
- Act as a technical mentor, supporting and guiding other Analytics Engineers
- Work closely with Data Engineers, BI teams and business stakeholders to deliver impact
Your Skills & Experience
- Strong commercial experience as an Analytics Engineer operating in a modern data platform
- Advanced data modelling capability, including dimensional and semantic modelling
- Strong PySpark development experience within a Lakehouse environment
- Advanced SQL skills for complex transformations
- Hands-on experience with Databricks, including pipelines and governance tooling
- A strong engineering mindset, with a focus on reliability, testing and maintainability
- Proven ability to mentor others and provide technical leadership
- Comfortable operating at the intersection of business and engineering
Senior Analytics Engineer in London employer: Energy Jobline ZR
As a Senior Analytics Engineer in London, you will join a large and dynamic organisation that prioritises innovation and collaboration within its analytics function. With a strong commitment to employee growth, the company offers a supportive work culture that encourages technical ambition and mentorship, alongside competitive benefits and a hybrid working model. This is an exciting opportunity to influence data governance and engineering practices at a pivotal moment in the company's evolution.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Analytics Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with current employees at companies you're eyeing. A friendly chat can sometimes lead to opportunities that aren't even advertised!
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your best work, especially any projects related to analytics engineering. This gives potential employers a taste of what you can do beyond just your CV.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions and scenarios specific to analytics engineering. Practise explaining your thought process clearly, as communication is key in this role.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining us. Plus, it helps us keep track of your application and get back to you faster.
We think you need these skills to ace Senior 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 advanced SQL and PySpark skills, as well as any relevant experience with Lakehouse architecture. We want to see how you can contribute to our analytics function!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about the role and how your background aligns with our needs. Don’t forget to mention your experience in mentoring and collaborating with teams, as that’s key for us.
Showcase Your Projects:If you've worked on any relevant projects, make sure to include them in your application. Whether it's data modelling or building transformation pipelines, we love seeing real examples of your work and how you’ve made an impact in previous roles.
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’re considered for the role. Plus, it gives you a chance to explore more about our company culture and values!
How to prepare for a job interview at Energy Jobline ZR
✨Know Your Lakehouse Inside Out
Make sure you understand the Lakehouse architecture and how it integrates with data modelling. Brush up on your knowledge of SQL and PySpark, as you'll likely be asked to discuss how you've used these tools in past projects.
✨Showcase Your Mentorship Skills
Since this role involves mentoring other Analytics Engineers, be prepared to share examples of how you've supported colleagues in the past. Highlight any specific instances where your guidance led to improved outcomes or enhanced team performance.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions during the interview. Review advanced data modelling techniques and be ready to explain your approach to ensuring data quality and reliability in your pipelines.
✨Demonstrate Your Collaborative Spirit
This role requires working closely with Data Engineers and BI teams. Be ready to discuss how you've successfully collaborated across different teams in previous roles, and how you can contribute to a culture of collaboration in their organisation.
