Role Description Unser Bereich Customer Management entwickelt und betreibt auf einer skalierenden AWS-Infrastruktur eine wachsende Zahl an datengetriebenen Lösungen mit signifikantem Business Impact. Dafür arbeiten Platform Engineers, Data Engineers und Data Scientists gemeinsam an modernen Technologien, um unseren Fachabteilungen maßgeschneiderte, datengetriebene Services zur Verfügung zu stellen. - Produktentwicklung und Betrieb: Du stellst den reibungslosen operativen Betrieb eines AWS-basierten Produkts zur Ermittlung von Kundenpräferenzen sicher und entwickelst dieses stetig weiter. - End-to-End Projektarbeit: Von der Konzeption bis hin zur Analyse begleitest Du Projekte gemeinsam im Team. - Data Engineering: Die Entwicklung unserer Daten-Pipelines liegt in Deiner Hand. - Datenqualität und Betrieb: Du etablierst Datenqualitätsprüfungen, Monitoring und Alerting und unterstützt beim Incident-Handling. - Produkt und Business-Logik: Du implementierst Business-Logik im Produkt und stellst sicher, dass Anforderungen sauber in Datenstrukturen und Transformationen übersetzt werden. - Stakeholdermanagement: Du stehst im engen Austausch mit den Fachbereichen zur Abstimmung von Betrieb und Plattform-Ausrichtung. Qualifications - Abgeschlossenes Studium (z. B. Physik, Mathematik, Informatik, Wirtschaftswissenschaften) oder eine vergleichbare Qualifikation. - Mindestens 3 Jahre Berufserfahrung als Analytics Engineer, Data Scientist, Data Engineer oder in einer vergleichbaren Rolle. - Sehr gute Python-Kenntnisse für die Pipeline- und Softwareentwicklung. - Gute SQL-Kenntnisse für die Datenmodellierung und -analyse. - Erfahrung im Aufbau, Betrieb und in der Wartung von Daten-Pipelines. - Routinierter Umgang mit modernen Software-Engineering-Praktiken (Git, Code Reviews, CI/CD, Testing). - Praktische Erfahrung mit AWS (z. B. Glue, Lambda, Step Functions); Kenntnisse in Spark/Databricks sind ein Plus. Benefits - Mobiles Arbeiten. - Flexible Arbeitszeiten. - Gesundheitsvorsorge. - Mitarbeitervergünstigungen. - Team- und Firmenevents. - Mitarbeiterfahrzeug. Contact Dennis Rahim HR-Manager

• Develop and maintain dbt models that transform event and customer data into reusable dimensions and facts • Implement consistent transformations for identifiers, tenancy, deduplication, and time-based logic • Implement and maintain dbt tests for schema, freshness, and business logic validation • Investigate and remediate issues surfaced by tests, monitoring, or product stakeholders • Ensure models, columns, and metrics are well documented and aligned with standards • Maintain tags, ownership metadata, and lineage annotations where applicable • Build and maintain tables used by dashboards, segmentation, Ads Manager reporting, and embedded analytics • Refactor ad hoc or feature-specific logic into standardized reusable assets • Coordinate with Product Data Engineering and the CDP team on schema changes, backfills, and event data issues • Partner with product engineers to validate outputs and support safe consumption patterns

• Design and maintain scalable data architecture, pipelines, and models using dbt, Airflow, and Redshift. • Build reusable datasets that power analytics, experimentation, and business intelligence. • Collaborate with Engineering to define event tracking and instrumentation standards. • Develop core metrics, dashboards, and self-serve tools for stakeholders. • Partner with Product, Marketing, and Engineering to uncover insights on user behavior and growth. • Ensure data quality, reliability, and governance through validation and testing. • Communicate findings clearly to technical and non-technical audiences. • Continuously improve workflows, documentation, and analytics processes.

• Define and govern the product event taxonomy across services and applications • Partner with engineering teams to establish clear instrumentation contracts and naming standards • Own the modeling patterns that translate event collection pipelines into durable warehouse datasets • Ensure event data is reliable, deduplicated, and usable for analytics and modeling • Transform raw events into reusable behavioral datasets such as sessions, feature usage, funnels, retention cohorts, and customer journeys • Design models that enable product teams to analyze feature adoption, engagement, and lifecycle behavior • Maintain modeling patterns that support both exploratory analysis and production use cases • Define and maintain canonical entities such as Agency, Location, Contact, Conversation, Campaign, Spend, Usage, and Outcomes • Establish durable fact and dimension models that connect behavioral events to business entities • Ensure relationships between entities remain consistent and scalable across teams and product surfaces • Build warehouse models that power product analytics platforms • Ensure metrics in analytics tools and warehouse metrics resolve to the same definitions • Provide standardized datasets for funnels, cohorts, retention analysis, and product experimentation • Build behavioral and feature‑ready datasets used by data science for lifecycle modeling, experimentation, and prediction • Ensure datasets are stable, versioned, and reproducible for downstream ML workflows • Establish modeling patterns, dbt conventions, macros, and documentation standards used across analytics engineering • Design tenant‑safe models that support multi‑tenant workloads and high‑concurrency analytics • Partner with platform teams to ensure models are performant for both internal analytics and in‑app experiences • Define tests, freshness expectations, and invariants for behavioral datasets • Implement automated validation for event completeness and schema consistency • Partner with platform and engineering teams to detect and resolve issues before they impact analytics or customers • Establish reusable modeling patterns and best practices • Review work from analytics engineers and raise the bar for correctness, clarity, and maintainability • Help shape the long‑term architecture of the behavioral data platform