PPC Shopping Strategist - Remote & Data-Driven Growth

Genie Shopping

Cambridge

Hybrid

GBP 32,000 - 48,000

Full time

14 days+

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Benefits offered by this job

Remote working allowance
Flexible working
25 days annual leave
Workplace pension
Employee referral scheme
Cycle to work

Job summary

Genie Shopping is a UK-based performance marketing firm that manages Shopping campaigns for 1,000+ retailers across Google and Microsoft Ads. The role focuses on day-to-day management, performance monitoring, and data-driven optimisation within our distributed team.

You will work toward progressing into a PPC Manager role as you grow, with onboarding that emphasizes in-office collaboration in Cambridge before transitioning to a distributed model with periodic Cambridge visits.

Qualifications

  • Strong analytical skills to interpret data and identify trends.
  • Excellent attention to detail and accuracy in reporting.
  • Comfortable working with large datasets and spreadsheets.
  • Experience with PPC/eCommerce or performance marketing is a plus.

Responsibilities

  • Manage day-to-day Shopping campaigns across Google and Microsoft Ads.
  • Monitor account health and investigate performance changes.
  • Apply optimisations using established processes and frameworks.
  • Roll out updates, tests and new features across your accounts.
  • Collaborate with Paid Media and Partnerships to drive growth.

Skills

Analytical skills
Attention to detail
Data interpretation
Spreadsheet skills
Prioritisation
Independent worker
Curious mindset

Tools

Google Ads
Microsoft Ads
Google Merchant Center

Job description

Genie Shopping is a UK-based performance marketing firm that manages Shopping campaigns for 1,000+ retailers across Google and Microsoft Ads. The role focuses on day-to-day management, performance monitoring, and data-driven optimisation within our distributed team.

You will work toward progressing into a PPC Manager role as you grow, with onboarding that emphasizes in-office collaboration in Cambridge before transitioning to a distributed model with periodic Cambridge visits.

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