Auf einen Blick
- Aufgaben: Entwickle und pflege skalierbare Datenpipelines und Produkte für moderne Datenplattformen.
- Unternehmen: Respektierte internationale Finanzinstitution mit innovativen Projekten.
- Vorteile: Langfristiger Vertrag, wettbewerbsfähiges Gehalt, 50/50 WFH und Homeoffice-Möglichkeiten.
- Weitere Informationen: Exzellente Karrierechancen und Zugang zu modernen Datentechnologien.
- Warum dieser Job: Gestalte die Zukunft der Datenverarbeitung in einem dynamischen und kollaborativen Umfeld.
- Qualifikationen: Mindestens 5 Jahre Erfahrung in Data Engineering und starke Programmierkenntnisse.
Das prognostizierte Gehalt liegt zwischen 60000 - 84000 € pro Jahr.
Design, develop and maintain scalable end-to-end data pipelines and data products.
Build robust ETL/ELT solutions for data warehouse and data lake/lakehouse environments.
Collaborate with business analysts and stakeholders to gather, refine and translate requirements into technical solutions.
Develop high-quality, maintainable code using modern software engineering practices.
Contribute to data architecture, platform design and technical decision-making.
Implement data integration, transformation and validation processes across multiple data sources.
Support real-time and batch data processing initiatives.
Participate in proof-of-concept activities and evaluation of emerging data technologies.
Ensure solutions meet security, governance, data retention and performance requirements.
Support production environments, including participation in an on-call rotation.
Minimum 5 years of experience in Data Engineering, Big Data Engineering, or Analytics Engineering roles.
Strong experience designing and developing data ingestion and processing pipelines.
Hands-on experience with modern data platforms and technologies such as: Spark, Flink, Kafka or similar distributed processing frameworks, Hadoop ecosystem technologies including Hive, HBase, Impala or equivalent or lakehouse technologies such as Apache Iceberg.
Strong programming skills in Python, Java or C#.
Excellent understanding of data modelling, scalable system design and data architecture principles.
Strong SQL development skills and experience working with relational and/or NoSQL databases.
Experience within Investment Banking, Capital Markets, Financial Services, Risk, Trading or Market Data environments is mandatory.
Fluent in English.
Nice to have:
- Experience with workflow orchestration tools such as Airflow, NiFi or RunDeck.
- Exposure to CI/CD pipelines and modern software delivery practices.
- Experience working in Agile environments (Scrum/Kanban).
- Knowledge of automated testing, debugging and performance optimisation.
- Experience with cloud-based data platforms such as Databricks, Azure Synapse or Snowflake.
- Understanding of microservices and API technologies.
Long-term contract for 24 months with a highly respected international financial institution (strong chance to extend up to five years due to project timelines).
Opportunity to work on large-scale data transformation initiatives.
Exposure to modern data technologies and enterprise-scale data platforms.
Collaborative and intellectually stimulating environment.
Competitive contract package.
50/50 split WFH/home office per week (and possibility to work abroad 20 days per year).
If you possess strong Data Engineering expertise combined with experience in financial services and modern data platforms, we would be pleased to hear from you before the deadline on 16 June.
Data Analytics Engineer / Ingenieur Arbeitgeber: Swisslinx
Unser Unternehmen bietet eine herausragende Arbeitsumgebung für Data Analytics Engineers, die an der Spitze der Dateninnovation in der Finanzbranche stehen möchten. Mit einem starken Fokus auf Zusammenarbeit und persönlichem Wachstum fördern wir eine Kultur, die kreative Lösungen und den Austausch von Ideen schätzt. Unsere Mitarbeiter profitieren von flexiblen Arbeitsmodellen, der Möglichkeit, an spannenden Großprojekten zu arbeiten, und einer langfristigen Perspektive in einem dynamischen internationalen Umfeld.
