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.
As a Senior Growth Engineer, you will operate at the intersection of software engineering, marketing technology, data engineering, and product analytics. You will build the technical foundations that enable Growth teams across Acquisition, Activation, Retention, Sales, and Partnerships to move faster, measure performance accurately, and experiment with confidence. The role combines hands-on engineering with architecture ownership, advanced tracking, attribution infrastructure, and AI-powered workflows. You will work on complex measurement challenges in an increasingly privacy-constrained environment, ensuring that critical growth signals remain reliable and actionable. You will also partner closely with commercial and technical stakeholders to translate business hypotheses into scalable engineering solutions. This is a high-ownership opportunity for an engineer who enjoys ambiguity, modern data systems, distributed integrations, and applying AI where it creates meaningful business value.
Accountabilities
- 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.
Requirements:
- 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.
- Hands-on experience with modern tag management and data activation platforms such as RudderStack, HubSpot, or equivalent technologies is advantageous.
Benefits:
- Remote work flexibility, with the freedom to work from home.
- 25 working days of paid vacation.
- Jornada Intensiva in August.
- Premium health, dental, and mental health coverage through Alan, including coverage for pre-existing conditions.
- €150/month meal allowance through the available benefits card.
- 50% discount on prepared dishes at the office.
- Flexible remuneration options for additional meal expenses and public transportation, subject to applicable limits.
- Home office equipment, including a table, ergonomic chair, and monitor.
- Free Spanish language classes.
- Cash referral rewards for successful talent referrals.
- Daily office breakfast and monthly team events.
- International and multicultural working environment with colleagues representing more than 60 nationalities.
- Opportunity to work on high-impact growth, marketing technology, data, and AI initiatives.
- Benefits may vary depending on the type of contract issued.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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