Bangalore, KA, IN
Application Engineer II
Application Engineer II
About the Team
Our motto is: “Delivering trusted data and expertise.”
We sit at the core of Asset Management data processing, responsible for generating the golden copy for security master data, credit ratings, prices, and other market-related datasets.
We enable downstream systems and users through automated workflows and data maintenance processes — ensuring timely, accurate, and reliable delivery.
About the Role
As a member of our team, you will
- Act as part of a rotating Level 3 (L3) support role, helping users and downstream systems by analyzing data or timing issues and proposing sustainable solutions.
- Instruct and guide our SaaS vendor support teams on corrective actions, preparing documentation and handover materials as needed.
- Collaborate with business analysts and solution architects on designing, implementing, and testing new solutions aligned with business requirements.
- Gain a deep understanding of the end-to-end (E2E) landscape and support the testing and rollout of new products and asset classes.
- Represent the team in cross-functional initiatives, providing expertise, support, and capabilities to projects beyond our immediate squad.
About You
Essentials
- University degree or equivalent experience in a technical role
- 2–5 years of experience in data engineering or application development (data warehouse, ETL, MDM/EDM solutions)
- Strong command of SQL (query design, optimization, performance tuning)
- Hands-on experience with S&P Global EDM or a similar Enterprise Data Management / Master Data Management platform.
- Solid understanding of ETL/ELT concepts, data integration, and data flow design.
- Experience in database design (logical and physical), data warehousing, and metadata management
- Exposure to application development principles — APIs, modular design, version control
- Familiarity with versioning and CI/CD tools (Git, Azure DevOps, etc.) and Agile delivery methods (Scrum, Kanban)
- Good engineering skills and knowledge of at least one programming language (Python preferred)
- Strong analytical and problem-solving skills
- Ability to work under pressure
- Flexible and open for new tasks, willing to learn permanently
- Excellent team player attributes combined with a proactive approach and a 'can do' attitude
Nice to Haves
- Experience with cloud platforms (AWS, Azure) and modern data technologies (Databricks, Snowflake, Spark)
- Knowledge of EDM - specific tools (EDM Core Matcher, Load, Export, Workflow configuration)
- Familiarity with data governance and lineage tools
- Familiarity with Palantir Foundry — including data pipelines, ontology modelling, and operational workflows.
- Experience mentoring junior developers or contributing to technical strategy
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.
Reference Code: 135906
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Data Management, Data