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- Tasks: Design and build scalable data pipelines for advanced analytics and AI applications.
- Company: Join a growing team of AI specialists in a collaborative investment firm.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Why this job: Make a real impact by transforming data into valuable insights for investment decisions.
- Qualifications: Experience in Python, Azure, and data engineering tools like dbt and Airflow.
- Other info: Dynamic team culture with opportunities to shape analytics and decision-making.
The predicted salary is between 36000 - 60000 £ per year.
We are looking to expand our Data Engineering team to build modern, scalable data platforms for our internal investment desks and portfolio companies. You will contribute to the firm’s objectives by delivering rapid and reliable data solutions that unlock value for Cerberus desks, portfolio companies, and other businesses. You’ll do this by designing and implementing robust data architectures, pipelines, and workflows that enable advanced analytics and AI applications. You may also support initiatives such as due diligence and pricing analyses by ensuring high-quality, timely data availability.
What you will do:
- Design, build, and maintain scalable, cloud-based data pipelines and architectures to support advanced analytics and machine learning initiatives.
- Develop robust ELT workflows using tools like dbt, Airflow, and SQL (PostgreSQL, MySQL) to transform raw data into high-quality, analytics-ready datasets.
- Collaborate with data scientists, analysts, and software engineers to ensure seamless data integration and availability for predictive modeling and business intelligence.
- Optimize data storage and processing in Azure environments for performance, reliability, and cost-efficiency.
- Implement best practices for data modeling, governance, and security across all platforms.
- Troubleshoot and enhance existing pipelines to improve scalability and resilience.
Sample Projects You Work On:
- Financial Asset Management Pipeline: Build and manage data ingestion from third-party APIs, model data using dbt, and support machine learning workflows for asset pricing and prediction using Azure ML Studio. This includes ELT processes, data modeling, running predictions, and storing outputs for downstream analytics.
Your Experience:
We’re a small, high-impact team with a broad remit and diverse technical backgrounds. We don’t expect any single candidate to check every box below - if your experience overlaps strongly with what we do and you’re excited to apply your skills in a fast-moving, real-world environment, we’d love to hear from you.
- Strong technical foundation: Degree in a STEM field (or equivalent experience) with hands-on experience in production environments, emphasizing performance optimization and code quality.
- Python expertise: Advanced proficiency in Python for data engineering, data wrangling and pipeline development.
- Cloud Platforms: Hands-on experience working with Azure. AWS experience is considered, however Azure exposure is essential.
- Data Warehousing: Proven expertise with Snowflake – schema design, performance tuning, data ingestion, and security.
- Workflow Orchestration: Production experience with Apache Airflow (Prefect, Dagster or similar), including authoring DAGs, scheduling workloads and monitoring pipeline execution.
- Data Modeling: Strong skills in dbt, including writing modular SQL transformations, building data models, and maintaining dbt projects.
- SQL Databases: Extensive experience with PostgreSQL, MySQL (or similar), including schema design, optimization, and complex query development.
- Infrastructure as Code: Production experience with declarative infrastructure definition – e.g. Terraform, Pulumi or similar.
- Version Control and CI/CD: Familiarity with Git-based workflows and continuous integration/deployment practices (experience with Azure DevOps or Github Actions) to ensure seamless code integration and deployment processes.
- Communication and Problem solving skills: Ability to articulate complex technical concepts to technical and non-technical stakeholders alike. Excellent problem-solving skills with a strong analytical mindset.
About Us: We are a new, but growing team of AI specialists - data scientists, software engineers, and technology strategists - working to transform how an alternative investment firm with $65B in assets under management leverages technology and data. Our remit is broad, spanning investment operations, portfolio companies, and internal systems, giving the team the opportunity to shape the way the firm approaches analytics, automation, and decision-making.
We operate with the creativity and agility of a small team, tackling diverse, high-impact challenges across the firm. While we are embedded within a global investment platform, we maintain a collaborative, innovative culture where our AI talent can experiment, learn, and have real influence on business outcomes.
Data Engineer in London employer: Cerberus Capital Management
Contact Detail:
Cerberus Capital Management Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer in London
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even just grab a coffee with someone who’s already in the data engineering field. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Show Off Your Skills
Don’t just tell us what you can do; show us! Create a portfolio of projects that highlight your data engineering skills. Whether it’s a GitHub repo with your code or a blog post explaining a complex problem you solved, we love to see your work in action!
✨Ace the Interview
Prepare for those interviews like it’s a big exam! Brush up on your technical skills, but also be ready to discuss how you’ve collaborated with others. We want to see how you think and solve problems, so practice articulating your thought process clearly.
✨Apply Through Our Website
We’re always on the lookout for fresh talent, so don’t hesitate to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, you’ll get to learn more about our team and culture while you’re at it!
We think you need these skills to ace Data Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Data Engineer role. Highlight your technical expertise in Python, Azure, and data warehousing to catch our eye!
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're excited about this position and how your background fits into our team. Share specific examples of your past projects that relate to building scalable data pipelines.
Showcase Your Problem-Solving Skills: In your application, don’t shy away from discussing challenges you've faced in previous roles and how you overcame them. We love candidates who can articulate complex concepts and demonstrate strong analytical thinking.
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people!
How to prepare for a job interview at Cerberus Capital Management
✨Know Your Tech Stack
Make sure you’re well-versed in the tools mentioned in the job description, like dbt, Airflow, and Azure. Brush up on your SQL skills too, especially with PostgreSQL and MySQL, as you might be asked to solve real-world problems using these technologies during the interview.
✨Showcase Your Projects
Prepare to discuss specific projects you've worked on that relate to data engineering. Highlight your experience with building data pipelines, optimising performance, and any challenges you faced. This will demonstrate your hands-on experience and problem-solving skills.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You’ll likely need to communicate with both technical and non-technical stakeholders, so being able to articulate your thoughts clearly is key. Consider doing mock interviews to refine this skill.
✨Ask Insightful Questions
Prepare thoughtful questions about the team’s current projects, challenges they face, and how they measure success. This shows your genuine interest in the role and helps you assess if the company culture aligns with your values.
