Senior Financial Analyst I(PySpark data engineer)

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Apache Hadoop, Scala Programming, Machine learning techniques, Data science techniques, PyTorch, TensorFlow, MapReduce, R Programming

Job Description

Swiss Re is one of the world’s leading providers of reinsurance, insurance and other forms of insurance-based risk transfer, working to make the world more resilient. We anticipate and manage a wide variety of risks, from natural catastrophes and climate change to cybercrime. We cover both Property & Casualty and Life & Health. Combining experience with creative thinking and cutting-edge expertise, we create new opportunities and solutions for our clients. This is possible thanks to the collaboration of more than 14,000 employees across the world.

Our success depends on our ability to build an inclusive culture encouraging fresh perspectives and innovative thinking. We embrace a workplace where everyone has equal opportunities to thrive and develop professionally regardless of their age, gender, race, ethnicity, gender identity and/or expression, sexual orientation, physical or mental ability, skillset, thought or other characteristics. In our inclusive and flexible environment everyone can bring their authentic selves to work.

About the role:

The Performance Data Management (PDM) team is at the core of the Performance Management Hub (PMH) and focuses on analysing and providing insights on all aspects of the financial performance of the business unit Reinsurance. Core objective is the enabling of the performance analytics through sourcing various internal and external data sets, maintaining a tactical data environment, and gradually building out the scope of the analytics performed.

The core activities of the Data Engineer in the PDM team include:

• Build our data backbone by integrating various data systems using Python, PySpark and open source as well as proprietary tools
• Apply Big data analytics, data mining techniques for the gathering of data, and assess data validity
• Undertake requirements capturing on business questions to be addressed and related performance drivers and critical metrics
• Use knowledge of existing data to propose and provide insights on performance drivers
• Contributing to ad-hoc projects and supporting other team members as required

About the team:

The Performance Data Management team is at the core of the Performance Management Hub (PMH). The PMH team combines focused performance projects, the management of the BU's central initiatives, performance cycle, and innovative and insightful financial performance analytics. The PMH team sits within Reinsurance Financial Planning & Analysis (FP&A) and as such is an integral part of the strategic and financial management of the Business Unit (BU) Reinsurance.

About you:

• Overall 6 to 8 years of working experience in insurance analytics, data engineering or similar function
• Excellent knowledge of relational databases like SQL Server, PostgreSQL or MySQL
• Strong fundamentals in any programming language like Python or Scala
• Familiarity with Hadoop and Spark (PySpark, SparkSQL is considered a plus)
• Data Model Design and implementation based on the requirements
• Proficient with writing SQL queries, Stored Procedures and Views
• Familiarity with any ETL tool like SSIS
• Knowledge of current Visualization tools is an advantage.
• Familiarity in Palantir tools, Data mining skills would be preferred
• Knowing multiple technologies Excel macros, HTML, CSS, JavaScript is a plus.
• University degree (equivalent) in quantitative field (e.g. Mathematics, Statistics, Operations Research, Industrial engineering, Computer Science Engineering, Econometrics or Information Technology)

Specific soft skills

• Excellent command of spoken and written English and ability to present to senior management.
• Standout colleague with ability to build proactive, collaborative working relationships with peers and key partners based on respect and teamwork
• Inquisitive, proactive and willing to learn new technologies, understand insurance business and its economics
• Process and delivery mind-set aspiring for methodological and operational improvements
• Ability to drive the data engineering aspects of multiple ad-hoc projects and have good expectation management of partners.