Data Engineer II at Swiss Re United Kingdom

Bangalore, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

22 Feb, 26

Salary

0.0

Posted On

24 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, PySpark, Python, SQL, Data Modeling, ETL, Data Lakes, Data Warehousing, Agile, Problem Solving, Interpersonal Skills, Communication Skills, Financial Planning, Distributed Computing, Data Governance, Insurance Domain

Industry

Insurance

Description

We have opening for both Bangalore and Hyderabad As a Data Engineer, you will be responsible for designing and implementing complex data pipelines and analytics solutions to support key decision-making business processes. You will be expected to take end-to-end ownership of technical deliverables, gaining a full understanding of the data and the underlying business logic required to deliver the solutions. Key responsibilities include: Work closely with Product Owners and business analysts to understand requirements, formulate solutions, and evaluate the implementation effort. Design, develop and maintain scalable data transformation pipelines. Data model Design, Data architecture implementation Deliver scalable data solutions leveraging PySpark, Python and enterprise data platforms Evaluate new capabilities of the analytics platform, develop prototypes, and contribute to building a single source of truth for our application landscape. Collaborate within a global development team to design and deliver solutions Assist stakeholders with data-related functional and technical issues Ensure the implementation and adherence to SwissRe's data governance standards across data pipelines." About the Financial planning solutions team: Our team plays a critical role in delivering IT solutions and supporting business units across the organization. We are dedicated to enhancing financial planning tools and processes, ensuring they’re scalable and adaptable to meet the evolving needs of the business. Collaboration, transparency, and creativity are at the core of how we work, and you’ll find a supportive environment that encourages idea-sharing and mutual respect. About You: You will work as a member of our dynamic financial planning team. Your work will have a direct impact on streamlining processes, improving system functionality, and enabling data-driven decision-making across the business. You enjoy the challenge of solving complex big data analytics problems using state-of-the-art technologies as part of a growing global team of data engineering professionals. You are a self-starter with strong problem-solving skills and capable of owning and implementing solutions from start to finish. Key qualifications include: Bachelor's degree level or equivalent in Computer Science, Data Science or related field. ~5+ of experience working with large-scale software systems. Strong expertise in PySpark (minimum 3 years, preferably more) and SQL Proven experience designing and delivering large-scale data engineering solutions on enterprise data platforms and distributed computing (Spark/Hive/Hadoop preferred). Solid understanding of data management fundamentals, including data modeling, ETL, data lakes, and data warehousing. Experience with financial or operational planning systems or supporting planning & forecasting processes is a strong plus. Experience with Scrum/Agile development methodologies Strong analytical and problem-solving skills; self-starter with a positive attitude and ability to manage own workload. Proven experience working with a diverse multi-location team of internal and external professionals Good interpersonal and communication skills, with the ability to engage business stakeholders, justify architectural decisions, and explain complex technical solutions in clear business terms. Ability and enthusiasm to work in a global and multicultural environment Knowledge of Insurance Domain, Financial Industry or Finance function in other industries is a strong plus About Swiss Re 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 and their passion for sustainability.If you are an experienced professional returning to the workforce after a career break, we encourage you to apply for open positions that match your skills and experience. .swissre_footer { position: relative; margin-top: -50px; height: 30px; clear: both; margin-bottom: 20px; background: #EEE none repeat scroll 0% 0%; line-height: 30px; padding: 0px 10px; color: #AAA; font-family: "Arial,Helvetica,sans-serif"; } .swissre_jobtemplate { width: 970px; max-width: 100%; height: auto; } .jobDisplay .job { font-family: "Arial" !important; font-size: 12px !important; } .joqReqDescription { max-width: 100%; height: auto; align: center; } .joqReqDescription ul { width: 787px; max-width: 100%; } .joqReqDescription p { width: 827px; max-width: 100%; } Keywords: Reference Code: 135868

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Responsibilities

As a Data Engineer, you will design and implement complex data pipelines and analytics solutions to support key business processes. You will take end-to-end ownership of technical deliverables, ensuring adherence to data governance standards.