Role Description For our client in the telecommunications industry based in Bern, we are looking for an experienced, motivated, and proactive Senior Data Engineer (Kafka). In this project, we are looking for an experienced Senior Data Engineer with a strong focus on Kafka, data architecture, and modern data products. You will play a key role in designing, developing, and integrating scalable data solutions while helping to build and evolve a high-performance data platform. Working in a modern engineering environment, you will contribute to the implementation of event-driven architectures and efficient data processing pipelines. - Analyze and map existing data structures to a centralized data model - Design and define Kafka-based data products - Create event models, data contracts, topic structures, and best practices for Kafka implementations - Develop, implement, and integrate scalable data products using Kafka - Build and optimize event-driven data processing solutions - Ensure an automated data operations lifecycle, including development, deployment, validation, and monitoring - Collaborate closely with architects, developers, and data engineering teams to deliver robust data solutions - Contribute to the continuous improvement of data engineering standards and best practices Qualifications - University degree or equivalent qualification in Computer Science, Data Engineering, or a related field - Several years of experience as a Senior Data Engineer, Data Platform Engineer, or in a similar role - Strong hands-on experience with Apache Kafka, Kubernetes, and Elasticsearch - Expert knowledge of Kafka Streams, ksqlDB, or Apache Flink - Proven experience designing and implementing event-driven architectures and Kafka-based data products - Strong understanding of automated data operations lifecycles (Develop, Deploy, Validate, Observe) - Solid experience with search technologies, especially Elasticsearch - Analytical, structured, and solution-oriented working style - Excellent communication skills and a collaborative mindset - Fluent English (C2 level) 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.

• Manage and optimise Snowflake data warehouses. • Overhaul Snowflake warehouse performance through materialisation strategies, dynamic tables, and clustering optimisation. • Build scalable ETL/ELT pipelines using dbt, Airflow, Fivetran, and AWS DMS. • Build ingestion pipelines using DMS and Fivetran. • Develop modelling layers in dbt using medallion architecture principles. • Transform raw data into clean, reliable, and business-ready models using dbt and AI-assisted tooling for documentation and testing. • Integrate data from multiple sources, including CRM systems, payment platforms, gaming platforms, and other operational systems. • Own initiatives focused on data quality improvements and monitoring, including anomaly detection and automated alerting. • Monitor and optimise platform performance, cost efficiency, and security. • Work closely with cross-functional teams across product development, data, operations, and analytics functions. • Collaborate with the data science team and support colleagues with reporting and analytics activities when required. • Support the organisation’s move towards real-time data ingestion and ETL using technologies such as DMS, Kafka, and Kinesis. • Help mentor other engineers within the team. • Contribute to the team's AI strategy and promote effective use of AI tools across engineering workflows. • Produce and maintain clear, comprehensive documentation to support scalability, transparency, and long-term platform sustainability. • Communicate effectively with both technical and non-technical stakeholders and proactively raise blockers when encountered.

• Lead end-to-end data migration activities, including planning, mapping, transformation, validation, and reconciliation. • Define and execute data migration strategies and methodologies for Skadina implementations. • Oversee functional, integration, system, regression, and User Acceptance Testing (UAT) activities. • Ensure data quality, integrity, and consistency throughout migration and deployment phases. • Develop and review test plans, test cases, test scripts, and quality metrics. • Coordinate defect management, issue resolution, and root cause analysis activities. • Collaborate with architects, business analysts, developers, and business stakeholders to ensure successful project delivery. • Support cutover planning, deployment activities, and post-go-live stabilization. • Monitor project quality standards and ensure compliance with established processes and best practices. • Provide technical leadership and mentorship to project teams during migration and testing phases. • Prepare and maintain technical documentation, migration plans, and testing reports.

Role Description Enable Data is seeking an experienced Azure Data Architect with strong hands-on engineering expertise to design, develop, optimize, and modernize enterprise-scale data platforms. The role combines solution architecture, technical leadership, and individual contributor responsibilities, requiring deep expertise in Azure data services, data engineering best practices, and large-scale data processing using PySpark, Python, SQL, and Azure Databricks. The ideal candidate will be responsible for analyzing existing data products and pipelines, identifying improvement opportunities, leading refactoring and redesign initiatives, and implementing scalable, reliable, and high-performance data solutions on Azure. Responsibilities - Architecture & Solution Design - Overall 15+ years of experience with 10+ years of experience in Data Engineering and Analytics solutions. - Design end-to-end data architectures on Microsoft Azure. - Define data ingestion, transformation, storage, governance, and consumption strategies. - Create scalable, secure, and cost-effective data solutions aligned with business objectives. - Establish architectural standards, design patterns, and best practices for data engineering teams. - Collaborate with business stakeholders, product owners, and technical teams to translate requirements into technical solutions. - Design, develop, and modernize enterprise data platforms on Azure. - Analyze, refactor, and redesign existing data pipelines and products while building new scalable data solutions. - Act as a hands-on individual contributor with technical leadership responsibilities, including mentoring junior developers and driving best practices. - Strong hands-on experience in PySpark, Python, and SQL. - Expertise in Azure Databricks and Azure data ecosystem. - Experience designing and implementing scalable data architectures. - Ability to analyze, optimize, refactor, and redesign existing data pipelines. - Develop and maintain ETL/ELT solutions and data products. - Performance tuning, troubleshooting, and data quality implementation. - Mentor and guide junior developers and conduct code reviews. - Collaborate with cross-functional teams to deliver end-to-end data solutions. Requirements - Azure Data Lake, Azure Data Factory, Azure Synapse. - Data warehousing, lakehouse architecture, and cloud migration. - CI/CD, Git, and Agile development practices. Role Type - Senior Individual Contributor with Technical Leadership responsibilities.