At a Glance
- Tasks: Lead a diverse team of data analysts and BI experts in exciting projects.
- Company: Join a global company focused on data-driven solutions and innovation.
- Benefits: Enjoy a collaborative culture, remote work options, and opportunities for professional growth.
- Why this job: Perfect for those who thrive on challenges and want to make an impact in marketing analytics.
- Qualifications: Experience in data & analytics, coaching, and familiarity with SAS, PowerBI, and Python required.
- Other info: Work with a multicultural team from 7 different nationalities.
The predicted salary is between 43200 - 72000 £ per year.
🚀 Analytics Engineer Manager – London Hybrid 💰 £100-125KOur client, a fast-growing SaaS platform, is on a mission to transform their industry through data-driven innovation. They are looking for an Analytics Engineering Manager to take full ownership of their analytics engineering function, designing scalable data models, optimising transformations, and enabling high-quality insights across the business.As the owner of Analytics Engineering, you will be the bridge between raw data and actionable intelligence. You’ll shape the company’s analytics strategy, partner with stakeholders to understand data needs, and deliver well-modeled, documented, and trustworthy datasets for both internal and customer-facing analytics solutions.You’ll also spearhead the migration to a new self-service BI tool, ensuring the analytics layer is clean, well-structured, and ready to support data-driven decision-making at scale. Working as part of a small, high-impact team, you’ll have the autonomy to innovate and set the gold standard for analytics engineering in the organisation.Tech Stack & Responsibilities 📊 90% SQL-focused work with Python for custom tooling & ingestion workflows 📈 dbt for transformations & data modelling to create analytics-ready datasets 🔄 Building and maintaining reliable, scalable ELT pipelines 📊 Designing and implementing data models optimised for self-service and ML use case What We’re Looking For ✅ Expert-level SQL skills & strong Python for automation and tooling ✅ Hands-on experience with Cloud tech ✅ Deep understanding of analytics engineering best practices, including data modelling methodologies (Kimball, Star Schema, Inmon) ✅ An architecture mind to lead technical strategy ✅ Proactive problem solver with experience in start-ups or small data teams 📍 London Hybrid – No sponsorship availableThis is your chance to own and shape the analytics function in a high-impact role within a scaling SaaS business. If you’re looking for autonomy, technical leadership, and the opportunity to deliver business-changing insights—this is it! 🚀You could be a Lead Analytics Engineer, Senior Analytics Engineer, Head of Data leading a small team. Someone with a passion for developing a team and still wants to be a key hands on player.At Cognify we have many AE roles on so please apply even if this one isn’t quite the best fit
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LinkedIn Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Team Lead
✨Tip Number 1
Familiarise yourself with the tools mentioned in the job description, especially SAS, PowerBI, and Python. Having hands-on experience or even completing relevant online courses can give you a significant edge during interviews.
✨Tip Number 2
Highlight your coaching experience by preparing specific examples of how you've successfully led teams in the past. Be ready to discuss your leadership style and how it has positively impacted team performance.
✨Tip Number 3
Showcase your analytical mindset by discussing previous projects where you used data to drive decisions. Prepare to explain your thought process and how you approached problem-solving in those situations.
✨Tip Number 4
Emphasise your customer-oriented approach by sharing examples of how you've collaborated with product managers or business development teams. This will demonstrate your ability to align your team's work with broader business goals.
We think you need these skills to ace Data Team Lead
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data analytics, coaching, and any relevant tools like SAS, PowerBI, and Python. Use specific examples to demonstrate your leadership skills and analytical mindset.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention your previous experience in marketing and how it aligns with the responsibilities of the Data Team Lead position. Be sure to showcase your ability to work with diverse teams.
Showcase Relevant Projects: If you have worked on projects involving data pipeline development or analysis using Azure and Databricks, include these in your application. Describe your role and the impact of your contributions to highlight your expertise.
Highlight Soft Skills: Since the role involves coaching and collaboration, emphasise your soft skills such as communication, teamwork, and customer orientation. Provide examples of how you've successfully led teams or worked with stakeholders in the past.
How to prepare for a job interview at LinkedIn
✨Showcase Your Coaching Experience
Since the role involves leading a team, it's crucial to highlight any previous coaching or mentoring experience. Share specific examples of how you've helped team members grow and develop their skills in data analytics.
✨Demonstrate Technical Proficiency
Familiarity with SAS, PowerBI, Python, and Azure is essential for this position. Be prepared to discuss your hands-on experience with these tools and how you've used them in past projects to drive results.
✨Emphasise Collaboration Skills
This role requires close collaboration with product managers and business development teams. Prepare to discuss how you've successfully worked with cross-functional teams in the past and how you can ensure alignment between data analysis and business needs.
✨Highlight Your Analytical Mindset
An analytical mindset is key for this position. Be ready to provide examples of how you've approached complex data problems, the methodologies you used, and the impact your analyses had on decision-making processes.