• Data platform engineering: Design and maintain scalable batch and near-real-time data pipelines across mobile applications, NFC/fuel transactions, station integrations, ERP integrations, payments, support systems, and operational databases. • Data modeling: Create clean, reusable data models for core entities such as customers, vehicles, drivers, stations, transactions, wallets, limits, invoices, products, maintenance services, and geographic coverage. • Reliability and quality: Implement data validation, lineage, observability, alerting, reconciliation, and automated quality checks to ensure business-critical dashboards and reports are accurate and timely. • Analytics enablement: Partner with analytics, product, finance, operations, and customer success teams to deliver self-service datasets, metrics layers, and well-documented data marts. • Performance and cost optimization: Tune queries, storage layouts, orchestration schedules, and cloud resources to improve platform performance and manage infrastructure cost. • Data governance and security: Apply data access controls, PII handling, retention practices, auditability, and compliance-aware engineering patterns across the data lifecycle. • Integration engineering: Build robust ingestion patterns for APIs, webhooks, CDC, files, event streams, third-party integrations, and partner station data feeds. • DevOps for data: Use CI/CD, version control, automated testing, infrastructure-as-code, and deployment standards for data pipelines and transformations. • Incident management: Troubleshoot data incidents, conduct root-cause analysis, reduce recurring failures, and communicate impact clearly to stakeholders. • Technical mentorship: Review designs and code, establish engineering standards, mentor junior team members, and raise the quality bar for data engineering at PetroApp.

• Own Databricks production support for the company's data platform, including monitoring, alerting, and incident response across all production data flows. • Maintain and report on SLA performance metrics for data pipeline delivery, ensuring visibility into platform health and accountability across internal and external stakeholders. • Identify and implement pipeline optimizations that reduce Databricks compute costs, improve throughput, and reduce processing windows while tracking impacts through measurable KPIs. • Migrate legacy ETL/ELT pipelines to Databricks, building automation tooling to reduce manual intervention and ensure uninterrupted data delivery during transitions. • Support new customer onboarding by provisioning, validating, and hardening tenant data pipelines that deliver reliable, isolated data from day one. • Design and build high-performance Databricks pipelines that ingest, transform, and serve ERP and CRM data at scale across both Azure and AWS environments. • Own the Delta Lake architecture, including schema design, partitioning strategies, data quality enforcement, and incremental processing patterns. • Enforce data security best practices across Databricks environments, including role-based access control, secrets management, and compliance requirements for enterprise business data. • Implement data quality monitoring and observability across pipeline health and ML model inputs, ensuring data integrity that directly supports predictive analytics. • Apply and enforce multi-tenant data isolation patterns, ensuring reliable and secure data delivery across enterprise customers. • Partner with the Enterprise Architecture team to ensure data pipelines integrate seamlessly with the broader AI and analytics ecosystem. • Support a globally distributed operation through on-call rotation and after-hours incident response, meeting SLAs across multiple time zones. • Maintain technical documentation, runbooks, and architectural decision records, contributing to team knowledge sharing and operational readiness across on-call and incident response scenarios. • Apply CI/CD best practices to data pipeline development, including version control, automated testing, and deployment tooling to ensure reliable and repeatable pipeline delivery.

Role Description - Entwicklung, Betrieb und kontinuierliche Weiterentwicklung moderner Data-Warehouse-Lösungen - Konzeption, Implementierung und Optimierung von skalierbaren Datenpipelines und zentralen Datenflüssen - Sicherstellung eines stabilen, performanten und qualitativ hochwertigen Datenbetriebs - Analyse komplexer Geschäftsanforderungen und Entwicklung datenbasierter Lösungen für unterschiedliche Fachbereiche - Modellierung und Integration von Daten aus verschiedenen Quellsystemen - Optimierung bestehender ETL-/ELT-Prozesse hinsichtlich Performance, Wartbarkeit und Datenqualität - Mitarbeit bei der Modernisierung von Datenplattformen sowie der Migration bestehender Datenlösungen in Cloud-Umgebungen - Enge Zusammenarbeit mit Fachbereichen, Data Analysts und weiteren Stakeholdern zur Umsetzung neuer Datenanforderungen - Unterstützung bei der kontinuierlichen Weiterentwicklung der Datenarchitektur und der technischen Standards Qualifications - Mehrjährige Erfahrung als Data Engineer mit Fokus auf Data Warehouse - Fundierte Erfahrung im Aufbau, der Weiterentwicklung und dem Betrieb von Data-Warehouse-Lösungen - Sehr gute Kenntnisse in SQL sowie in der Entwicklung und Optimierung von ETL-/ELT-Prozessen - Erfahrung in der Datenmodellierung, beispielsweise nach Data Vault - Erfahrung mit Cloud-basierten Datenplattformen, idealerweise im Microsoft Azure Umfeld - Kenntnisse in Azure Databricks oder vergleichbaren modernen Data-Engineering-Plattformen - Analytische Denkweise sowie die Fähigkeit, komplexe Geschäftsanforderungen in nachhaltige Datenlösungen zu übersetzen - Erfahrung bei der Modernisierung von Datenlandschaften und der Migration von Legacy-Systemen ist ein Plus - Selbstständige, strukturierte und lösungsorientierte Arbeitsweise - Kommunikationsstärke sowie Freude an der Zusammenarbeit mit interdisziplinären Teams - Hohe Eigeninitiative und Verantwortungsbewusstsein - Fliessend in Deutsch und Englisch Company Description Die Coopers Group AG ist eine agile Schweizer Recruiting Agentur, die Spezialisten und Führungskräfte in den Bereichen IT, Life Sciences, Engineering und Finance vermittelt. Mit flexiblen Ansätzen bringen wir Kandidat:innen und Unternehmen zusammen, die nicht nur fachlich, sondern auch menschlich zusammenpassen.

• Design, build, and maintain scalable, reliable, and automated data pipelines using SQL, Python, and Databricks to support enterprise analytics. • Architect and optimize robust data models and infrastructure to ensure high data quality, integrity, and accessibility across the client's ecosystem. • Partner closely with the Data Science team to operationalize their work, deploying statistical and machine learning models into production environments using DataOps best practices. • Identify, design, and implement internal process improvements, including automating manual data processes and optimizing data delivery for scalability. • Collaborate with cross-functional teams to identify business problems, gather requirements, identify data sources, and provide data-driven solutions.