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- Tasks: Lead a dynamic analytics engineering team and transform complex data into actionable insights.
- Company: Join a cutting-edge AdTech company at the forefront of data innovation.
- Benefits: Competitive salary up to £100,000, hybrid work model, and opportunities for professional growth.
- Why this job: Make a real impact by bridging technical teams and senior stakeholders in a fast-paced environment.
- Qualifications: 6+ years in analytics or data engineering, with strong SQL and Python skills.
- Other info: Exciting opportunity for career advancement in a collaborative and innovative setting.
The predicted salary is between 60000 - 84000 £ per year.
This is an Analytics Engineering Manager role leading a growing analytics engineering function at the intersection of data engineering, analytics, product, and advertising performance, within an AdTech business. The focus of the role is on turning complex, high-volume data into trusted, scalable analytics that support commercial decision-making. You’ll own the analytics engineering roadmap, lead delivery outcomes, and act as the bridge between technical teams and senior stakeholders, ensuring data is reliable, well-governed, and actionable.
The Role & Responsibilities:
- Lead and develop the analytics engineering team, owning delivery quality and outcomes
- Own and execute the analytics engineering roadmap, balancing new capability with technical debt
- Design and evolve ELT pipelines, data warehouse models, and analytical structures
- Ensure reliability and consistency of advertising and performance data across multiple platforms
- Act as the primary interface between engineering, analytics, product, operations, and commercial teams
- Translate business requirements into clear technical plans and priorities
- Define and enforce data governance standards, including testing, documentation, lineage, and observability
- Improve operational efficiency through automation, validation, and anomaly detection
- Maintain hands-on involvement in critical pipeline design, data modelling, and optimisation work
- Drive continuous improvement across tooling, processes, and team capability
Skills & Experience:
- 6+ years’ experience across analytics engineering, data engineering, or data platform roles
- Proven experience leading and developing technical data teams
- Expert SQL skills and strong Python capability
- Hands-on experience with dbt (or similar) and modern cloud data warehouses - the preference being BigQuery
- Experience with Looker advantageous
- Strong understanding of advertising and performance data, including measurement and attribution
- Experience building and scaling data governance, testing frameworks, and CI/CD for analytics
- Demonstrated ability to own roadmaps, prioritisation, and delivery in complex environments
- Strong communicator, able to engage both technical teams and senior stakeholders
- Comfortable operating in fast-paced, cross-functional environments
Analytics Engineering Manager in London employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Analytics Engineering Manager in London
✨Tip Number 1
Network like a pro! Reach out to folks in the AdTech space on LinkedIn or at industry events. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream role.
✨Tip Number 2
Prepare for those interviews by brushing up on your technical skills. Since this role is all about analytics engineering, make sure you can talk confidently about SQL, Python, and data governance. We want you to shine when discussing your hands-on experience!
✨Tip Number 3
Showcase your leadership skills! Be ready to discuss how you've developed teams and driven delivery outcomes in the past. We’re looking for someone who can bridge the gap between technical teams and stakeholders, so highlight those experiences.
✨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 Analytics Engineering Manager in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Analytics Engineering Manager. Highlight your experience in analytics engineering, data governance, and team leadership. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about this role and how you can contribute to our analytics engineering function. Let us know what excites you about working in AdTech!
Showcase Your Technical Skills: Don’t hold back on your technical prowess! Be sure to mention your SQL and Python skills, as well as any hands-on experience with dbt or cloud data warehouses like BigQuery. We love seeing those details!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep everything organised and ensures your application gets the attention it deserves. We can't wait to hear from you!
How to prepare for a job interview at Harnham
✨Know Your Data Inside Out
Make sure you’re well-versed in the specifics of analytics engineering, especially around ELT pipelines and data governance. Brush up on your SQL and Python skills, and be ready to discuss how you've used them in past projects.
✨Showcase Your Leadership Skills
As a potential manager, it’s crucial to demonstrate your ability to lead and develop teams. Prepare examples of how you've successfully managed technical teams, balanced new capabilities with technical debt, and driven delivery outcomes.
✨Bridge the Gap
Highlight your experience in acting as a liaison between technical teams and senior stakeholders. Be prepared to discuss how you’ve translated business requirements into actionable technical plans, ensuring everyone is on the same page.
✨Continuous Improvement Mindset
Discuss your approach to driving continuous improvement in processes and tooling. Share specific instances where you’ve implemented automation or validation techniques that enhanced operational efficiency in your previous roles.
