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Analytics Engineer II Are you passionate about turning complex data into intelligent, business-ready analytics products Do you thrive at the intersection of data engineering, visualization, and AI If you love building things that matter - at scale, across borders, and with cutting-edge technology - this could be your next great move. About the Role As an Analytics Engineer II , you will be at the heart of building modern analytics products that power smarter decisions across a global reinsurance business. From designing scalable data pipelines and semantic models to delivering interactive Power BI solutions and AI-enabled applications, you will work across the full analytics lifecycle. Leveraging platforms such as Palantir Foundry and Microsoft Power BI , you will translate complex business challenges into elegant, high-impact analytical solutions - and you'll do it with the help of the latest AI-assisted development tools. This is a role for someone who is curious, technically strong, and excited about applying emerging technologies to real-world problems. Key Responsibilities Design and develop enterprise Power BI semantic models, dashboards, and analytics applications that deliver clear, actionable insights to business stakeholders Build and maintain scalable data pipelines and transformation logic using Python/PySpark, SQL, and modern analytics platforms such as Palantir Foundry Partner with business stakeholders to deeply understand business problems and translate them into well-architected analytical products Architect analytics solutions that are performant, reusable, maintainable, and built to scale across a global data landscape Leverage AI technologies - including Palantir AIP and generative AI - to enhance analytics solutions, automate workflows, and improve user experiences where appropriate Utilize AI-assisted development tools such as GitHub Copilot or equivalent to improve development efficiency while upholding high engineering standards Collaborate within a globally distributed development team to deliver robust, scalable data products in an agile environment About the Team Life & Health Data & Analytics Engineering is a strategic technology partner for Swiss Re's Life & Health Reinsurance division. We are at the forefront of transforming the data landscape - building innovative analytical products and capabilities that enable better, faster, data-driven decisions across the entire business value chain. We are a large, globally distributed team working in an agile delivery environment, united by a shared passion for data, engineering excellence, and continuous innovation. Whether we're modernizing data infrastructure, developing intelligent applications, or exploring the latest in AI-assisted analytics, we bring curiosity and craftsmanship to everything we build. Joining this team means becoming part of a collaborative, inclusive, and forward-thinking community that values diverse perspectives and bold ideas. About You You are energized by the challenge of solving complex, large-scale analytics problems using leading-edge technologies. You have a proven ability to pick up new tools and platforms quickly - especially in the areas of AI, modern analytics, and cloud-native data engineering. You are a self-starter who takes ownership of solutions from concept to delivery, and you bring strong communication skills that allow you to bridge the gap between technical teams and business stakeholders. You are comfortable working across time zones and cultures, and you bring a collaborative, growth-oriented mindset to everything you do. We are looking for candidates who meet these requirements: Bachelor's degree or equivalent in Computer Science, Data Science, or a related discipline 3-5 years of experience working with large-scale software systems, with a focus on visualization tools such as Microsoft BI Suite, Azure Databricks, or Oracle Strong enterprise Power BI expertise , including semantic model design, DAX, Power Query, and visualization best practices Strong SQL skills with demonstrated experience designing performant queries and data models Hands-on experience developing reusable ETL/ELT pipelines Proficiency in Python/PySpark for data engineering, analytics, or automation tasks Solid understanding of dimensional modeling and data warehousing concepts Experience with Git and collaborative software development practices Familiarity with Agile software delivery methodologies These are additional nice to haves: Experience with modern analytics platforms such as Palantir Foundry, Databricks, Microsoft Fabric, or Snowflake Experience building data pipelines within Palantir Foundry Experience using Palantir AIP or other AI/LLM frameworks to develop intelligent business applications Experience .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.