Marketing Analytics Team Lead

Role overview

This is a senior analytics leadership opportunity for a marketing professional based in Germany. You will guide a team focused on turning marketing data into clear, actionable direction for a fast-growing digital business. The role sits at the intersection of marketing, analytics, and growth, with responsibility for the full measurement stack: attribution, tracking, dashboards, forecasting, and data foundations. You will work closely with Growth, Product, and Data Engineering teams to keep marketing data accurate, consistent, and usable across channels. The role suits someone who enjoys building scalable analytical systems, coaching people, and influencing business decisions through data.

This position is listed through a partner organization that oversees the application process and all following steps.

Key responsibilities

• Create and maintain a single, organization-wide framework for data-backed marketing decisions, including core metrics, reporting rules, dashboard standards, and analytics practices.
• Manage and grow a team of marketing and web analysts by setting priorities, assigning responsibilities, and supporting development through mentoring and structured growth plans.
• Take ownership of marketing attribution and performance measurement across affiliate, arbitrage, SEO, influencer, and related channels, with accurate spend allocation and audience segmentation.
• Define data requirements for engineering teams so pipelines, campaign structures, and marketing datasets remain dependable and high quality.
• Oversee web analytics work such as event tracking, funnel analysis, and behavioral insights to improve conversion outcomes.
• Work across functions with traffic acquisition, product, growth, and data teams to deliver insights that inform strategic decisions.
• Keep documentation for marketing analytics processes and systems organized, complete, and regularly updated.

Requirements

• At least 4 years of experience in marketing analytics or product analytics, including prior responsibility for leading or managing analysts, preferably in performance-driven or iGaming settings.
• Proven ability to build analytics capabilities or frameworks from the ground up, from gathering requirements through to delivering reporting and insights.
• Strong knowledge of traffic acquisition models such as CPA, hybrid, and rev-share, along with marketing attribution methods.
• Advanced SQL ability for extracting, transforming, and analyzing data.
• Practical experience with dbt, Git, and modern data warehouse environments.
• Hands-on use of BI platforms such as Tableau, including designing scalable dashboards with clear business value.
• Solid grasp of statistics and predictive modeling concepts, including LTV and revenue forecasting, with the ability to guide model design and assess outputs.
• Excellent communication and presentation skills, with the ability to turn complex analytical findings into clear business recommendations.
• Nice-to-have: experience with web analytics tools like Google Analytics and Google Tag Manager.
• Nice-to-have: knowledge of Python and exposure to ML-style modeling such as LTV prediction or user segmentation.

Benefits

• Flexible working arrangement with either fully remote work or hybrid options, depending on preference.
• Employment support in Switzerland or Poland, including help with administrative and legal steps.
• 20 paid vacation days, public holidays, and sick leave.
• Health insurance coverage plus psychological support.
• A monthly flexible benefits allowance for hobbies, sports, or personal interests.
• Access to learning and development resources such as courses, workshops, mentoring, and corporate language training.
• Regular team events, workshops, and company gatherings, both online and in person.
• A modern, automation-focused environment with limited bureaucracy, strong efficiency, and high autonomy.

Additional information

The hiring process is handled by the partner company, which also makes the final decision and organizes interviews or assessments. Applications are screened through an AI-assisted matching process to compare candidates against the role’s main requirements. Personal data is processed to evaluate candidacy and may be shared with the employer in line with applicable data protection rules, including GDPR. AI tools may also assist with application review and consistency checks, but human recruiters make the final hiring decision.