Beschreibung
Our client, a global leader in insurance, is looking for a (Senior) Spark Data Engineer to join the engineering team for a Global Finance Transformation Programme which is responsible for the finance integration of all special case legal entities (eg, new acquisitions and joint ventures). For the data engineering use-case, we use Azure Databricks, Azure Data Factory and Azure Data Lake (Delta Lake) to extract, load and transform data from various source systems and deliver it in a harmonized data model into the new global finance platform built on SAP FPSL.
Must have skills:
- 3+ years of experience in data engineering application development using Spark, preferably in an enterprise environment
- 5+ years of experience in building enterprise big data/data engineering applications using continuous integration tools like Azure DevOps
- Strong understanding of reusable software design patterns
- Design and implementation of production-grade solutions
- Delivering high-quality code, focusing on simplicity, performance, maintainability and scalability
- Practical experience in applying an agile way of working and in applying DevOps methodologies
- Excellent analytical and conceptual skills to understand complex technology stacks and their dependencies
Nice to have skills, or willingness to learn:
- Proficient in Python/PySpark and SQL (Spark SQL)
- Experience in Azure Data Factory, Azure Data Lake, Azure Monitor, Azure Log Analytics
- Enterprise system integration experience
- Insurance know-how
- Finance & accounting or actuarial knowledge
This position is a 100% remote project and requires an EU passport/work permit/visa.
Darwin Recruitment AG is a Zurich based, SECO licensed, privately owned subsidiary of Darwin Professional Staffing Group Ltd (a Global IT Recruitment Consultancy).
Darwin Recruitment AG manages client relationships whilst also utilising Darwin Professional Staffing Group databases and networks to source Candidates and fulfil client requests.
We do not ask for a placement fee from Candidates/Employees.