At a Glance
- Tasks: Design and build scalable Snowflake data models and pipelines for investment analytics.
- Company: Dynamic consultancy working with a boutique asset management firm in London.
- Benefits: Competitive daily rate, hybrid work model, and opportunity to shape data strategies.
- Other info: Join a forward-thinking team focused on eliminating data silos and enhancing operational efficiency.
- Why this job: Transform data processes and drive impactful investment decisions with cutting-edge technology.
- Qualifications: Experience in Snowflake, ELT/ETL workflows, and real-time data streaming.
Tired of untangling legacy data silos? Ready to build something that actually drives investment decisions? We are working with a London-based consultancy deployed at a boutique asset management firm. They aren't just moving data around; they are implementing Snowflake from the ground up to sharpen investment analytics, secure data sharing, and completely transform their operational efficiency. This is where you come in. You won't be babysitting old systems. You'll be designing the models and pipelines that make real-time data flow seamlessly.
What you’ll be doing:
- Designing, building, and maintaining scalable Snowflake data models and pipelines.
- Driving robust ELT/ETL workflows with a heavy focus on dbt.
- Implementing real-time or near real-time data streaming (using Kafka or Kinesis).
- Streamlining engineering processes to permanently eliminate data silos.
- Bonus: Integrating Aiviq native solutions (if you have this experience, we definitely want to talk to you).
Snowflake Data Engineer - Asset Management employer: Talenting Career Science
Join a forward-thinking consultancy that values innovation and collaboration, where your expertise as a Snowflake Data Engineer will directly impact investment decisions at a boutique asset management firm in London. Enjoy a hybrid work environment that promotes work-life balance, alongside competitive daily rates and opportunities for professional growth in a dynamic sector. Here, you will not only enhance your technical skills but also contribute to transformative projects that redefine operational efficiency.
StudySmarter Expert Advice🤫
We think this is how you could land Snowflake Data Engineer - Asset Management
✨Tip Number 1
Network like a pro! Reach out to your connections in the asset management and data engineering space. Attend meetups or webinars where you can chat with industry folks. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Snowflake projects, especially any cool models or pipelines you've built. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Brush up on your ELT/ETL workflows and be ready to discuss how you've tackled data silos in the past. Practising common interview questions can help you feel more confident when it’s time to shine.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, applying directly can sometimes give you a leg up in the hiring process.
We think you need these skills to ace Snowflake Data Engineer - Asset Management
Some tips for your application 🫡
Show Your Passion for Data:When you're writing your application, let your enthusiasm for data engineering shine through. We want to see how excited you are about building scalable Snowflake models and transforming data processes. Share any relevant projects or experiences that highlight your passion!
Tailor Your CV and Cover Letter:Make sure to customise your CV and cover letter for this role. Highlight your experience with Snowflake, dbt, and any real-time data streaming tools like Kafka or Kinesis. We love seeing how your skills align with what we're looking for, so don’t hold back!
Be Clear and Concise:Keep your application clear and to the point. We appreciate well-structured applications that make it easy for us to see your qualifications. Use bullet points where necessary and avoid jargon unless it’s relevant to the role.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at Talenting Career Science
✨Know Your Snowflake Inside Out
Make sure you brush up on your Snowflake knowledge before the interview. Understand its architecture, features, and how it integrates with ELT/ETL workflows. Be ready to discuss your experience with designing data models and pipelines, as well as any specific projects you've worked on that showcase your skills.
✨Showcase Your Problem-Solving Skills
Prepare to talk about challenges you've faced in previous roles, especially related to data silos or inefficient processes. Use the STAR method (Situation, Task, Action, Result) to structure your answers, highlighting how you drove solutions that improved operational efficiency.
✨Get Familiar with Real-Time Data Streaming
Since the role involves real-time data streaming, make sure you understand the concepts behind Kafka and Kinesis. Be prepared to discuss any relevant experience you have with these technologies, and think of examples where you've implemented or optimised data streaming solutions.
✨Ask Insightful Questions
Interviews are a two-way street, so come armed with questions that show your interest in the company and the role. Ask about their current data challenges, how they envision the Snowflake implementation evolving, or what success looks like for this position. This not only demonstrates your enthusiasm but also helps you gauge if the role is the right fit for you.
