At a Glance
- Tasks: Lead the creation of clean, reusable data models and ensure consistent metrics across the business.
- Company: Dynamic analytics team within a forward-thinking company, embracing innovation.
- Benefits: Competitive salary, remote work flexibility, and opportunities for professional growth.
- Other info: Join a collaborative environment where your analytical expertise will shape the future.
- Why this job: Make a real impact by transforming messy data into trusted insights that drive business decisions.
- Qualifications: Strong SQL skills, Power BI experience, and a knack for data modelling.
The predicted salary is between 90000 - 90000 € per year.
They've reached the point where reporting can't just be powered by clever people knowing where to look. The business needs a proper analytical layer. Clean models, agreed definitions, trusted Power BI datasets and fewer arguments over whose number is right.
You'll be the most senior technical Analytics Engineer in the team, owning how data is modelled, structured and made usable for analysts, reporting teams and senior stakeholders. The work sits close to the business. You'll be asking how things are counted, why teams measure things differently, what should become a shared metric, and how to build that into something reusable rather than another one-off query.
What you'll be doing:
- Owning the analytical / semantic data layer
- Designing clean, reusable models for reporting and analytics
- Defining business metrics and making sure they are used consistently
- Building trusted Power BI datasets and semantic models
- Creating documentation so analysts and stakeholders know what they are using
- Improving data quality, governance and reporting consistency
- Guiding analysts on SQL, modelling, query structure and best practice
- Acting as the bridge between engineering, analytics and the business
What they're looking for:
- Strong SQL
- Good Power BI experience, especially datasets / semantic models
- Experience modelling data for analytics, reporting or BI
- Azure data platform exposure
- Strong understanding of data modelling principles, ideally Kimball / semantic modelling
- Experience working directly with business stakeholders to define metrics and resolve inconsistencies
- Someone comfortable taking messy business questions and turning them into clean, reusable data structures
The bigger thing is being able to bring order to data that a lot of people depend on. You'll suit this if you like the modelling, definition and structure side of analytics as much as the output people see at the end.
Lead Data Analytics Engineer in Manchester employer: Socium
As a Lead Data Analytics Engineer at our company, you'll be part of a dynamic and innovative team that values collaboration and continuous improvement. We offer a supportive work culture that encourages professional growth through mentorship and access to cutting-edge tools, all while working remotely or occasionally in Manchester. Join us to make a meaningful impact by transforming complex data into clear insights that drive business success.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Analytics Engineer in Manchester
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data models and Power BI dashboards. 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 common analytics questions and scenarios. Think about how you would tackle messy business questions and turn them into clean, reusable data structures.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Lead Data Analytics Engineer in Manchester
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Lead Data Analytics Engineer. Highlight your SQL skills, Power BI experience, and any work you've done with data modelling. We want to see how your background aligns with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data analytics and how you can bring order to messy business questions. We love seeing your personality come through, so don’t hold back!
Showcase Relevant Projects:If you've worked on projects that involved building trusted datasets or defining business metrics, make sure to mention them. We want to know how you've tackled similar challenges in the past and what impact your work had!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates. Plus, it shows you're keen on joining the StudySmarter team!
How to prepare for a job interview at Socium
✨Know Your Data Modelling Principles
Make sure you brush up on data modelling principles, especially Kimball and semantic modelling. Be ready to discuss how you've applied these in past projects, as this will show your depth of knowledge and ability to bring order to messy data.
✨Showcase Your SQL Skills
Prepare to demonstrate your SQL expertise during the interview. You might be asked to solve a problem or optimise a query on the spot, so practice common SQL scenarios and be ready to explain your thought process clearly.
✨Familiarise Yourself with Power BI
Since the role heavily involves Power BI, ensure you can talk about your experience with datasets and semantic models. Bring examples of how you've built trusted datasets and improved reporting consistency in previous roles.
✨Communicate with Stakeholders
Be prepared to discuss how you've worked with business stakeholders to define metrics and resolve inconsistencies. Highlight your ability to translate complex data questions into clean, reusable structures, as this is key for the role.
