Growth Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Growth Engineer based in Germany.

• Build and maintain robust server-to-server integrations with major advertising and marketing platforms, including Meta CAPI, Google server-to-server integrations, and AppsFlyer.
• Own end-to-end web and application tracking parity, smart script implementations, and relevant mobile measurement configurations such as SKAN.
• Establish and maintain marketing tracking standards covering event naming conventions, schema versioning, consent management, and implementation governance.
• Serve as a technical authority for tracking quality, ensuring instrumentation is consistent, reliable, scalable, and aligned with established standards.
• Engineer and continuously improve the data infrastructure supporting attribution models, including integrations with advertising spend APIs, session data, and commercial metrics.
• Develop infrastructure that enables profit and gross-margin signals to be shared with advertising platforms to support value-based bidding and more effective acquisition strategies.
• Productionize machine learning models from early prototypes into reliable, monitored pipelines, including CLTV scoring, causal impact tooling, and models for non-trackable conversions.
• Build AI-assisted growth engineering tools, such as automated spend anomaly detection and LLM-powered systems for managing and improving tracking taxonomies.
• Develop pipeline infrastructure supporting lead scoring, intent signal enrichment, and CRM data quality.
• Build and maintain integration layers that enable experimentation with AI-powered sales and growth tools.
• Implement advanced matching, first-party data enrichment, and probabilistic matching techniques to improve signal quality as third-party tracking becomes increasingly limited.
• Act as a technical partner to Growth squads by resolving integration challenges, reviewing instrumentation, and identifying opportunities to replace bespoke implementations with scalable shared infrastructure.
• Collaborate with Data Engineering, Analytics, Growth, Sales, Partnerships, and other commercial stakeholders to translate business hypotheses into precise technical requirements.
• Document architectural decisions, integration patterns, technical standards, and data practices to create scalable and accessible institutional knowledge.
• Promote strong engineering practices around testing, reliability, maintainability, monitoring, and production ownership across growth technology systems.

• Strong professional experience in software engineering, with a track record of building clean, tested, maintainable, production-grade systems.
• Strong understanding of software design principles and experience applying Domain-Driven Design concepts to data, growth, or business systems.
• Extensive experience building and operating integrations using REST APIs, webhooks, event-driven architectures, or similar distributed systems technologies.
• Strong understanding of the failure modes, reliability considerations, and operational challenges associated with distributed systems and third-party integrations.
• Deep knowledge of modern marketing tracking, including browser and in-app consent frameworks, cookie-less measurement, server-side tagging, and the impact of privacy changes such as iOS restrictions on attribution.
• Strong data engineering capabilities, including advanced SQL, data modeling, pipeline architecture, and data reliability practices.
• Ability to work comfortably with Data Engineers and Analysts and translate analytical requirements into robust technical implementations.
• Experience working with commercial stakeholders such as Sales, Partnerships, and Growth leadership, with the ability to turn commercial hypotheses into clear technical requirements.
• Practical experience taking machine learning models into production, including monitoring, drift detection, fallback mechanisms, reliability, and clear ownership.
• Familiarity with modern AI engineering approaches, including LLMs, embeddings, vector search, and AI-assisted development tools.
• Strong engineering judgment and the ability to distinguish where AI can provide meaningful value from situations where conventional engineering approaches are more appropriate.
• Strong communication and collaboration skills, with the ability to operate effectively across technical and business teams.
• Comfortable working independently, taking ownership of complex problems, and making sound technical decisions in ambiguous environments.
• Familiarity with causal inference techniques such as difference-in-differences, synthetic control, or uplift modeling is a strong advantage.
• Experience building or scaling shared platform infrastructure within a high-growth consumer or B2B SaaS environment is a plus.