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- Tasks: Design and optimise data pipelines for banking and machine learning.
- Company: Join a leading investment bank in the heart of London.
- Benefits: Enjoy flexible working with 3 days on-site and competitive perks.
- Why this job: Be part of innovative projects that shape the future of finance.
- Qualifications: 5+ years in data engineering with strong skills in cloud platforms and programming.
- Other info: Mentorship opportunities available for junior engineers.
The predicted salary is between 48000 - 72000 £ per year.
Senior Data Engineer - Banking, Machine Learning sought by leading investment bank based in the city of London.
Inside IR35 - 3 days a week on site
We are seeking an experienced Data Engineer to design, build, and optimize our complex data pipelines and big data infrastructure. The ideal candidate will have deep expertise in architecting scalable data solutions, integrating machine learning workflows, and optimizing data processing systems. This role requires both technical excellence and the ability to influence architectural decisions that align with business objectives.
Key Responsibilities
- Design and implement robust, scalable data pipelines for processing large volumes of structured and unstructured data
- Architect end-to-end data solutions that support machine learning model training and deployment
- Develop and maintain data infrastructure that ensures data quality, reliability, and accessibility
- Optimize existing data workflows for performance, cost-efficiency, and maintainability
- Contribute to strategic architectural decisions and technical roadmaps
- Implement data governance and security best practices across the data ecosystem
- Mentor junior engineers and promote best practices in data engineering
Required Skills & Qualifications
- 5+ years of experience in data engineering roles with progressively increasing responsibility
- Proven experience designing and implementing complex data pipelines at scale
- Strong knowledge of distributed computing frameworks (Spark, Hadoop ecosystem)
- Experience with cloud-based data platforms (AWS, Azure, GCP)
- Proficiency in data orchestration tools (Airflow, Prefect, Dagster, or similar)
- Solid programming skills in Python, Scala, or Java
- Experience integrating ML workflows into production data systems
- Strong understanding of data modeling, ETL processes, and database design
- Demonstrated ability to architect solutions for big data challenges
Preferred Qualifications
- Experience with real-time data processing (Kafka, Kinesis, Flink)
- Knowledge of containerization and infrastructure-as-code (Docker, Kubernetes, Terraform)
- Familiarity with MLOps practices and tools (MLflow, Kubeflow, etc.)
- Experience with data governance frameworks and data cataloging
- Understanding of graph databases and unstructured data processing
- Knowledge of advanced analytics techniques and statistical methods
- Experience with data mesh or data fabric architectural patterns
Education
- Bachelor's degree in Computer Science, Data Science, or related field (Master's preferred)
- Relevant certifications in cloud platforms or data technologies
Please apply within for further details or call on Alex Reeder Harvey Nash Finance & Banking.
Contact Detail:
Harvey Nash Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer - Banking, Machine Learning
✨Tip Number 1
Network with professionals in the banking and data engineering sectors. Attend industry meetups or webinars to connect with potential colleagues and learn about the latest trends in data engineering and machine learning.
✨Tip Number 2
Showcase your technical skills through personal projects or contributions to open-source initiatives. This not only demonstrates your expertise but also gives you practical examples to discuss during interviews.
✨Tip Number 3
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as Spark, Airflow, and cloud platforms. Being able to speak confidently about these will set you apart from other candidates.
✨Tip Number 4
Prepare for technical interviews by practising common data engineering problems and scenarios. Focus on designing scalable data pipelines and integrating machine learning workflows, as these are key aspects of the role.
We think you need these skills to ace Senior Data Engineer - Banking, Machine Learning
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data engineering, particularly with complex data pipelines and machine learning workflows. Use specific examples that demonstrate your expertise in the required skills such as distributed computing frameworks and cloud-based platforms.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data engineering and how your background aligns with the responsibilities of the role. Mention any relevant projects or achievements that showcase your ability to design scalable data solutions and mentor junior engineers.
Highlight Relevant Skills: Clearly list your technical skills that match the job description, such as proficiency in Python, Scala, or Java, and experience with data orchestration tools. This will help your application stand out to hiring managers looking for specific qualifications.
Showcase Your Problem-Solving Abilities: Provide examples in your application that illustrate your problem-solving skills, especially in optimizing data workflows and implementing data governance practices. This will demonstrate your capability to contribute to strategic architectural decisions.
How to prepare for a job interview at Harvey Nash
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with data engineering, particularly in designing and implementing complex data pipelines. Highlight specific projects where you've used distributed computing frameworks like Spark or Hadoop, and be ready to explain your approach to optimising data workflows.
✨Demonstrate Machine Learning Integration
Since the role involves integrating machine learning workflows, come equipped with examples of how you've successfully incorporated ML into data systems. Discuss any challenges you faced and how you overcame them, as this will show your problem-solving skills.
✨Understand the Business Context
Research the investment bank and understand their business objectives. Be ready to discuss how your technical decisions can align with their goals. This shows that you’re not just a techie but also someone who understands the bigger picture.
✨Prepare for Scenario-Based Questions
Expect scenario-based questions that assess your ability to architect solutions for big data challenges. Practice articulating your thought process and decision-making when faced with hypothetical situations related to data governance, security, and performance optimisation.
