At a Glance
- Tasks: Design and build data pipelines on a modern Lakehouse and AI platform.
- Company: Goldman Sachs, a leading global investment banking firm.
- Benefits: Competitive salary, health benefits, and opportunities for professional growth.
- Other info: Dynamic work environment in London with excellent career advancement potential.
- Why this job: Join a cutting-edge team and work with innovative data technologies.
- Qualifications: Strong skills in Python or Java, SQL, and experience with Spark and Kafka.
The predicted salary is between 63000 - 77000 £ per year.
Goldman Sachs is seeking a Data Engineer to design, build and operate data pipelines on a modern Lakehouse and AI data platform in London.
You will contribute to ingestion, transformation, modelling and data quality, delivering reliable datasets for analytics and AI use cases.
Ideal candidates have strong Python or Java, SQL skills, experience with Spark, Kafka and Snowflake, and are comfortable with CI/CD and platform tooling.
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Data Platform Engineer — Lakehouse & AI employer: Goldman Sachs Group, Inc.
Goldman Sachs is an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation in the heart of London. With comprehensive training and mentorship, employees are empowered to develop their skills and advance their careers while building valuable relationships across the firm. The opportunity to work in such a prestigious environment not only enhances professional growth but also provides a unique platform for impactful contributions within the financial sector.
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We think this is how you could land Data Platform Engineer — Lakehouse & AI
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We think you need these skills to ace Data Platform Engineer — Lakehouse & AI
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
